Air quality monitoring system and method

ABSTRACT

In one illustrative configuration, an air quality monitoring system may enable wide-scale deployment of multiple air quality monitors with high-confidence and actionable data is provided. Further, the air quality monitoring system may enable identifying a target emission from a plurality of potential sources at a site based on simulating plume models. The simulation of plume models may take into consideration various simulation parameters including wind speed and direction. Further, methods of determining a plume flux of a plume of emissions at a site, and methods of transmitting data from an air quality monitor are disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.18/205,445 filed on Jun. 2, 2023, entitled “AIR QUALITY MONITORINGSYSTEM AND METHOD”, which is a continuation of U.S. patent applicationSer. No. 18/098,430, entitled “AIR QUALITY MONITORING SYSTEM AND METHOD”filed on Jan. 18, 2023 and issued on Sep. 12, 2023 as U.S. Pat. No.11,754,495, which is a continuation of U.S. patent application Ser. No.17/843,613 filed on Jun. 17, 2022 and issued on Feb. 28, 2023 as U.S.Pat. No. 11,592,390, which is Continuation of U.S. patent applicationSer. No. 17/843,554, filed on Jun. 17, 2022 and issued on Feb. 21, 2023as U.S. Pat. No. 11,585,752, entitled AIR QUALITY MONITORING SYSTEM ANDMETHOD, which is a continuation of U.S. patent application Ser. No.17/541,693, filed on Dec. 3, 2021 and issued on Jun. 21, 2022 as U.S.Pat. No. 11,366,057, entitled “AIR QUALITY MONITORING SYSTEM AND METHOD”which is a continuation of PCT/US2021/049702, entitled “AIR QUALITYMONITORING SYSTEM AND METHOD” filed on Sep. 9, 2021.

This application is related to issued U.S. patent application Ser. No.16/188,793, filed on Nov. 13, 2018 and issued on Apr. 28, 2020 as U.S.Pat. No. 10,634,558, entitled “AIR QUALITY MONITORING SYSTEM ANDENHANCED SPECTROPHOTOMETRIC CHEMICAL SENSOR,” which is hereby expresslyincorporated by reference in its entirety for all purposes.

This application is related to issued U.S. patent application Ser. No.16/823,205, filed on Mar. 18, 2020 and issued on Dec. 29, 2020 as U.S.Pat. No. 10,876,890, entitled “AIR QUALITY MONITORING SYSTEM ANDENHANCED SPECTROPHOTOMETRIC CHEMICAL SENSOR,” which is hereby expresslyincorporated by reference in its entirety for all purposes.

The present application is related to issued U.S. patent applicationSer. No. 16/953,908, filed on Nov. 20, 2020 and issued on Dec. 7, 2021as U.S. Pat. No. 11,193,822, entitled “AIR QUALITY MONITORING SYSTEM ANDENHANCED SPECTROPHOTOMETRIC CHEMICAL SENSOR,” which is hereby expresslyincorporated by reference in its entirety for all purposes.

PCT/US2021/049702 and the present application claim priority to U.S.Patent Application Ser. No. 63/076,829, filed on Sep. 10, 2020, entitled“AIR QUALITY MONITORING SYSTEM, ENHANCED SPECTROPHOTOMETRIC CHEMICALSENSOR, AND RELATED TECHNOLOGIES,” which is hereby expresslyincorporated by reference in its entirety for all purposes.

PCT/US2021/049702 and the present application claim priority to U.S.Patent Application Ser. No. 63/233,694, filed on Aug. 16, 2021, entitled“AIR QUALITY MONITORING SYSTEM AND Method”, which is hereby expresslyincorporated by reference in its entirety for all purposes.

A portion of the disclosure of this patent document contains material,which is subject to copyright and/or mask work protection. The copyrightand/or mask work owner has no objection to the facsimile reproduction byanyone of the patent document or the patent disclosure, as it appears inthe Patent and Trademark Office patent file or records, but otherwisereserves all copyright and/or mask work rights whatsoever.

TECHNICAL FIELD

This disclosure pertains generally, but not by way of limitation, tosystems and methods for reducing fugitive emissions. In particular, thesystem(s) and method(s) described herein provide remote monitoring offacilities and/or equipment that often emit gasses.

BACKGROUND

Air quality is one of the most important factors that can affect thehealth of a population. Countries around the world spend significantresources on monitoring air quality and controlling air pollution. Oneof the major problems is that instruments that can accurately monitorair quality are expensive and typically require expertise to operateproperly. Currently, air quality monitoring is mainly performed bygovernment agencies and dedicated organizations using specializedinstrumentation. As a result, general air quality data often does notprovide the fidelity necessary to pinpoint issues at a scale smallerthan a regional level. Real-time air quality monitoring at a finer scalemay be cost prohibitive because air quality monitoring instruments canbe expensive.

There are three types of sensing systems that are generally used for themeasurement for detection of compounds in air: point sensors, line(including long open path) sensors and imaging sensors. These systemscan be statically field-deployed, integrated into handheld devices, ormounted on various vehicles, such as automobiles, drones, and otherunmanned aircraft (such as balloons), planes, helicopters, and othermanned aircrafts, and on satellites. Static line or imaging sensors canalso be mounted on motorized systems to point toward different fields ofview of a site.

SUMMARY

Various illustrative configurations of air quality monitoring system andmethod are disclosed. The air quality monitoring system and method maydetect and report pollution from monitored site for a variety of reasons(e.g., corporate performance, quality of environment, regulatoryrequirements, etc.). For example, a monitored site may be an oilfacility removing natural gas (and/or oil) from an underground reservoirutilizes equipment (e.g., pumpjacks, holding tanks, valves, pipes, etc.)that requires maintenance. Occasionally, this equipment releasespollution into the atmosphere. This release into the atmosphere iscalled ‘fugitive gas emission’ or generically ‘pollution’ that should bedetected and reported so corrective action may be taken.

To address emission/pollution, the disclosed system monitors, detects,and reports the differential concentrations of gas from a population ofmonitors located around the perimeter of a site. Differentialconcentrations of gas from a population (of monitors located in amonitored area) indicates presence of a leak. An oil facility configuredwith the present system can sense the increase/presence of emissions bycomparing readings from a plurality of pollution monitors. Eachpollution monitor utilizes a logic control system to read at least onepollution sensor; furthermore, the aggregation of pollution monitorspresents the sensed site data to determine if there is a leak. Thepollution leak can be addressed accordingly (e.g., noted, repaired,observed, etc.).

At least some embodiments are air quality monitoring systems configuredfor wide-scale deployment of monitors with enough accuracy formeaningful and actionable data. In one aspect, an advanced technique isused to calibrate low-precision gaseous chemical sensors to obtainaccurate measurements by cross-calibrating those sensors to correctsensitivities to parameters that cause errors in measurement of targetedchemicals. In another aspect, air quality measurements are used toidentify sources of chemicals at a localized level by accounting forlocal conditions using data such as ambient condition data anduser-provided data about the local environment. In yet another aspect, agaseous chemical sensor with an improved encasement having a cell forreflecting and lengthening light path is provided to reduce thelimitations and enhance the accuracy of a conventionalspectrophotometric gaseous chemical sensor.

The system and components described herein can reduce the resources(e.g., instrument setup time, cost, expertise) that are needed to deploya large-scale air quality monitoring system and to increase fidelity ofair quality data. Reducing the need for resources also enables new waysof gathering air quality data, such as by crowd-sourcing data frominstruments deployed by non-expert users. The technology also serves todemocratize air quality monitoring by making air quality instrumentationand analysis affordable to individual users.

In some embodiments, a system can monitor one or more sites based on airquality. The system can include one or more monitoring elements capableof obtaining information, including air information, weatherinformation, environment information, user-inputted information,predictions, etc. The monitoring elements can be configured to detect,for example, the presence of gases, concentrations of gases, aircharacteristics, emissions, or the like. Based on the detection by themonitoring elements, the system provides analytics of the site(s). Forexample, the monitoring elements can be sensors that analyze air todetect emissions caused by equipment leaks at a site (e.g., gasproduction site, manufacturing site, recycling center, power plant,refinery, etc.). The system can be an air monitoring system that obtainsinformation about airborne particulate matter, gaseous emissions, orother desired information.

The air quality monitoring system and components further relate to thequantification, qualification, and/or localization of airborneparticulate matter or gaseous emissions in the atmosphere using one ormore point sensors configured to detect, measure, and/or sense one ormultiple target compounds and/or to other compounds present in theatmosphere. The air quality monitoring system may use as few as onestatic, point sensor system that measures gas concentration as well asone or more environmental conditions (e.g., weather conditions) toqualify, quantify, and/or localize emissions in a broad area around thesensor system. In one embodiment, the air quality monitoring system mayrely on multiple measurements by the sensor system over time from afixed location, together with a fluid mechanics-based atmosphericsimulation of the deployment site which integrates the weatherinformation collected by the sensor system to infer, using an inversemodel, spatially resolved information about the emission location,composition, flux and/or other parameters of interest for facilitatingmaintenance, tracking asset integrity, and evaluating environmental,health and safety impacts. Using a reduced number of sensors to achievequantification, localization and qualification of emissions is ofimportance to reduce the cost of the technology, which is the firstbarrier to mass adoption of real-time emissions monitoring systems. Themonitoring system allows for the use of the time resolution of thesensing system to evaluate and enhance the spatial resolution(localization) of emissions sources and the precision of concentrationmaps (quantification) of allowed or fugitive emissions (qualification).Further, the monitoring system proposes an enhanced, adaptive deploymentmethod for sensor networks using site information to minimize the numberof sensor systems to be deployed while meeting the site operatorrequirements regarding detection, quantification, qualification, andlocalization. Additional specific sensor system embodiments and methodsfor denoising and analyzing absorption spectra for improving speciationand long-term remote calibration and accuracy of a field-deployedspectrometer are disclosed.

With regard to detection, the method and sensor system has detectioncapabilities via adaptively modifying alert thresholds based onlocalization, quantification, and/or qualification of emissions.

With regard to localization, the system and methods can produce therequired spatial resolution by using intensive modeling. An inverseproblem strategy is employed, using frequent measurements of targetcompound concentrations, and co-located and contemporaneous weather datawith a detailed simulation model integrating the fluid mechanics of theatmosphere around and in the site.

A single point sensor system can be used to spatially resolve the siteand localize the emission sources. This localization is constructed overtime by analyzing the concentration measured by the system configuredwith a point sensor in various weather conditions. The simulation allowsa level of fidelity that is not possible through wind back-tracing,instead the present(s) system and method(s) integrates effects fromweather, turbulence, terrain rugosity, obstacles and topology, andequipment geometry on the transport of the compound. This isparticularly necessary for large sites such as landfills or otherdiffuse area sources of target compounds where wind patterns areinfluenced by the terrain (i.e., the landfill mound itself) and wherethe localization includes identifying hotspots amid diffuse sources.

With regard to qualification, multiple methods are proposed for theevaluation of the type and composition of emissions, particularly toseparate fugitive emissions from operational emissions and to qualifyemissions from diffuse sources. The emission qualification with respectto composition is possible in some embodiment of the disclosure usingbroadband spectrometry. The emission qualification with respect to type,for instance routine versus fugitive emission, is obtained by performinga statistical analysis of the detected emissions based on their source,magnitude, and frequency, compared to the source, magnitude, andfrequency of routine emissions. The probability of an emission being dueto unintended operations can be derived by statistical inference (i.e.,a deviation from the ‘routine’ emission profile).

Specific methods for enhancing the selectivity, calibration, precision,and accuracy of measuring the concentration of compounds absorbing lightin the mid-infrared range of the electromagnetic spectrum using abroadband, low spectral resolution spectrophotometer are proposed.Although the method may be detailed with respect to a specific sensortechnology embodiment, this is not intended to limit the disclosure tospectrophotometers since other types of sensors available on the marketcan be used in the general method for the quantification, qualification,localization, and detection of emissions in an atmospheric context.

In one embodiment, the system uses a broadband absorptionspectrophotometer as the point sensor. This spectrophotometer has a lowspectral resolution, which means that the profile of the gas isdetectable, but not the fine structure. Therefore, it is not possible tosimply use peak amplitude to infer a compound concentration because manycompounds may absorb that wavelength. The system and method include awavelet-based method that enhances selectivity, accuracy, and precisionby integrating sensor-specific information in pre-processing andidentification of the mixture composition and concentrations. Inparticular, the method can help distinguish unknown compounds in themixture even if such a compound interferes with the target compoundabsorption profile.

It can be difficult to quantify the flux, emission mass or rate, or sizeof a target gas emission event. This is because it is difficult torelate a certain concentration to an emission mass or rate. Based onweather conditions, the location of the emission source (with respect tothe sensor) and emission characteristics (temperature/pressuredifferential, diffusivity of the compound, etc.) of a certain measuredconcentration may not directly relate to the emission mass, flux, orvolume. The relationship between the concentration measurement and theflux, volume, or mass of the emission is therefore established bydefining a model, which depends on the type of sensing technology thatis used.

In certain cases, plume theory is used as the model. For point systems,the concentration of the target gas can be calculated by takingmeasurements at various positions across a transversal section of theemission plume. This can be done by using a dense network of pointmeasurements with static point sensors. For line sensors, a singlesensor can be used if the wind conditions are favorable and thepathlength completely passes across the plume. For mobile sensors, asingle point sensor can be moved across the plume to form either a pointcloud or a line across the plume. The objective is to measure aninstantaneous cross section of the plume such that the mass-flowconservation principle can be applied. Extrapolation or models can beused to fully integrate the plume cross section where measurements arenot available. For example, a line measurement across the plume togetherwith a Gaussian plume model approximation and some estimate of windspeed and direction can be used to estimate the flux.

The present disclosure discloses a method for determining concentrationsfrom a specific embodiment of the sensor technology and also methods forevaluating the flux, mass, and/or volume of emissions of a target gas.Two methods are provided as examples. One method is based on the inversemodel, where the transport problem is explicitly defined and therelation between flux, location, and concentration is obtained based onthe weather conditions and the simulation results. Another, simplermethod is based on wind direction and speed alone, where the winddirection is used instead of movement to create a cross section of theplume. The second method relies on a simplification assumption and maynot be usable in all conditions. In particular, the second method willbe less accurate than the first method (inverse method) when turbulencedominates the transport of airborne gases, such as methane and when thesource of such gases is too close to the sensor.

There are several strategies to deploy point sensor networks. Sensornetworks known in the industry focus on the network effect on somesensor characteristics by the redundancy of measurement between multiplesensors. For example, this is the strategy taken for obtaininglocalization using point sensors in most industry references. The systemand methods disclosed take the opposite approach, the objective beinginstead to minimize superimposition of the detection ranges of thesensors of the network, and to maximize coverage of potential sourceswhile minimizing the number of sensors used. The goal is to meet thenetwork requirements using the least resource(s). In one embodiment, amethod is proposed which fully characterizes the detection area of thesensor system based on not only the sensor characteristics (detectionlimit, compounds detectable, frequency of measurement), but also projectcharacteristics (fraction of emissions to be detected, localizationrequirement, report frequency requirement), site characteristics(location of equipment in the field, terrain topology, terrain cover androughness, historical weather patterns, area where the sensor canactually be deployed, and restricted deployment area), and/or priornetwork data (if a prior deployment configuration was in place).

Once the detection area for a sensor in the field is characterized, amethod is used to dynamically optimize the position of the sensornetwork that minimizes the number of sensor systems. For example, inupstream oil and gas, oil fields extend over large areas and are coveredwith hundreds of well pads. A network of sensors can be used to detectemissions across multiple well pads or well pad equipment groups with asingle sensor. Each sensor is defined by a detection range, the shapeand extent of which depends on the detection limit of the sensor unit,the smallest emission size to be detected (inferred by the fraction ofemission to be detected), the diffusivity of the emission compound, andthe wind speed. The terrain and surface roughness and land cover furtherinfluence the detection range, as well as the principal wind directionof the specific site (some wind directions can be only rarely observed).The localization is defined at the lowest level by the ratio of thedistance separating two sources and the distance to the sensor. If twosources are too close together, the measurement and/or simulation maynot be able to distinguish between them. Likewise, angulardiscrimination may matter; for example, if a small source is occulted bya larger, closer source and is close to being in the direction from thesensor. Based on the field information and the project characteristics,the position of the sensor is optimized by an algorithm that minimizesthe amount of redundant information captured by the sensors measurementand maximizes the project objectives.

The detection, quantification, qualification, and localization ofemissions is not always sufficient to provide the necessary actionableinformation about emitters of the target compounds in the environment;the operator may for instance lack the processes to seamlessly integratesuch emission information into their standard operation methodology andwork practices. What may be ascertained in the detection,quantification, qualification, and localization of emissions does notaddress operational integration and ultimately the utility of suchemission information. Disclosed are specific methods for operationalintegration of the emission information. In particular, a maintenancetriaging and tracking methodology as well as an emission abatementtracking methodology are proposed. Also disclosed is an actionabilityengine that proposes appropriate responses to the emissions and learnsfrom maintenance practices over time.

The air quality monitoring system may focus on, without being limitedto, anthropogenic and natural sources of atmospheric emissions. This isof particular interest in oil and gas applications such as at oil andgas extraction pads, in chemical production and transport activities,agricultural activities, and in the solid waste industry. The systemsand methods described makes static, real-time monitoring more accurateand affordable.

In one configuration, a computer-implemented method of identifying atarget emission at a site using a plurality air quality monitoring eachimplementing chemical sensors is disclosed. The method may includecreating at least one simulation model for the site based on simulationparameters. The simulation parameters may include at least two of a winddirection, a wind speed, an air pressure, an air temperature, a numberof potential emission sources, a location of each of the potentialemission sources, a source flux associated with each of the potentialemission sources, a surface concentration, a weather condition, ahygrometry data, and an altitude. The method may include obtainingactual parameters for the site corresponding to the simulationparameters, and receiving actual emissions measurements from a pluralityof air quality monitors deployed at the site associated with the actualparameters for the site. The plurality of air quality monitors may bedeployed at predefined locations at the site. The method may furtherinclude identifying a relevant simulation model from the at least onesimulation model, wherein simulation parameters associated with therelevant simulation model match with the actual parameters, extractingvirtual emissions measurements generated by the relevant simulationmodel. The method may further include receiving actual emissionsmeasurements from the plurality air quality monitors deployed at thesite associated with the actual parameters for the site, correlating thevirtual emissions measurements with the actual emissions measurementsfrom the plurality air quality monitors, and determining configurationof at least one emission source based on the correlation. Theconfiguration of emission sources may include a location of the emissionsource at the site and a concentration of emissions from the emissionsource.

In another configuration, a computer-implemented method for identifyinga source of a target chemical at a site is disclosed. The method mayinclude providing at least a predominate air quality monitor including afirst sensor responsive to the target chemical, and a first location atwhich a predominate air quality monitor is located. The method mayfurther include measuring a first concentration of the target chemicalat the predominate air quality monitor as a function of a wind speed awind direction. The wind speed and the wind direction may be measuredusing a wind sensor. The method may further include providing a plume ofthe target chemical. The plume may include a horizontal distributiondeviation defined as a standard deviation of a horizontal distributionof a plume concentration and a vertical distribution deviation definedas a standard deviation of a vertical distribution of the plumeconcentration. The method may further include creating at least onesimulation model for the site based on the above-mentioned simulationparameters. The method may further include identifying an emission rateof the target chemical at the source using the simulation modelfunctionally operated by the standard deviation of horizontaldistribution, the standard deviation of vertical distribution, the firstconcentration at the predominate air quality monitor, and the windspeed. The identified source may be outputted to a computer device.

In another configuration, a method of installing an air quality monitorsystem at a site is disclosed. The method may include surveying the siteby procuring an equipment log of a plurality of leak-prone equipment atthe site, a centroid of the leak-prone equipment, and a wind-rosediagram representative of wind at the site. The method may furtherinclude attaching the wind-rose diagram to the site. The wind-rosediagram may include a predominate downwind direction, a secondarydownwind direction angularly offset from the predominate downwinddirection, and a tertiary downwind direction angularly offset from thepredominate downwind direction and oppositely disposed from thesecondary downwind direction. The method may further include installinga predominate air quality monitor in the predominate downwind directionfrom the centroid at a location where the predominate air qualitymonitor has a maximal angular separation between the leak-proneequipment, installing a secondary air quality monitor in the secondarydownwind direction from the centroid where the secondary air qualitymonitor has minimal observational overlap with the predominate airquality monitor, and installing a tertiary air quality monitor in thetertiary downwind direction from the centroid where the tertiary airquality monitor has minimal observational overlap with the predominateair quality monitor and with the secondary air quality monitor.

In yet another configuration, a method of determining a plume flux of aplume of emissions at a site is disclosed. The method may includereceiving a predetermined number of samples, by a plurality of airquality monitors installed at the site, of the plume at a plurality ofangles of the plume, registering an associated concentration point basedon the plurality of angles, and obtaining a fit of a point cloud. Whenmeasurements occur in idealized conditions site parameters, the methodmay include calculating the plume flux using a mass conservationequation by multiplying an area concentration of the plume cross sectionby its normal speed and by estimating the plume concentration in aheight direction.

In another configuration, a method of transmitting data from an airquality monitor is disclosed. The method may include providing a memoryin the air quality monitor, and providing an emissions sensor in the airquality monitor. The emissions sensor may be configured to obtain sensordata at a predefined frequency. The memory may be configured to storesensor data obtained by the emissions sensor. The air quality monitormay be configured to transmit the sensor data to a cloud-base database.The method may further include detecting a low-connectivity condition,and upon detecting the low-connectivity condition, starting to store thesensor data in the memory. Further, the method may include detecting anormal-connectivity condition, and upon detecting thenormal-connectivity condition, transmitting the sensor data stored inthe memory to the cloud-based database.

In yet another configuration, a system of one or more computers can beconfigured to perform particular operations or actions by virtue ofhaving software, firmware, hardware, or a combination of them installedon the system that in operation causes or cause the system to performthe actions. One or more computer programs can be configured to performparticular operations or actions by virtue of including instructionsthat, when executed by data processing apparatus, cause the apparatus toperform the actions. One general aspect includes a computer-implementedmethod for identifying a source of a target chemical. Thecomputer-implemented method also includes providing at least apredominate air quality monitor may include: a first sensor responsiveto the target chemical. The method also includes a first location atwhich the predominate air quality monitor is located. The method alsoincludes measuring a first concentration of the target chemical at thepredominate air quality monitor as a function of a wind speed and/or awind direction. The method also includes where the wind speed and thewind direction are measured using a wind sensor. The method alsoincludes providing a plume of the target chemical, the plume may includea horizontal distribution deviation defined as a standard deviation of ahorizontal distribution of a plume concentration. The method alsoincludes a vertical distribution deviation defined as a standarddeviation of a vertical distribution of the plume concentration. Themethod also includes identifying an emission rate of the target chemicalat the source using a plume model functionally operated by a standarddeviation of horizontal distribution. The method also includes astandard deviation of vertical distribution. The method also includesthe concentration at the predominate air quality monitor. The methodalso includes the wind speed. The method also includes furtheridentifying the source from a plurality of possible sources of thetarget chemical by correlating the emission rate. The method alsoincludes outputting the identified source to a computer device. Themethod also includes providing a cloud server. The method also includeswhere the predominate air quality monitor further may include aprocessor. The method also includes an averaging routine operativelyassociated with the processor. The method also includes averaging aseries of the first concentration of the target chemical to generate anaveraged first concentration, according to at least one of a) with theprocessor at the predominate air quality monitor, b) according to theaveraging routine, and c) before transmitting the averaged firstconcentration to the cloud server. The method also includes aftertransmitting the first concentration to the cloud server, bootstrappinga plurality of the first concentration.

In another configuration, a computer-implemented method for identifyinga source of a target chemical. The computer-implemented method alsoincludes providing at least a predominate air quality monitor mayinclude a first sensor responsive to the target chemical. The methodalso includes a first location at which the predominate air qualitymonitor is located. The method also includes measuring a firstconcentration of the target chemical at the predominate air qualitymonitor as a function of: a wind speed and/or a wind direction. Themethod also includes where the wind speed and the wind direction aremeasured using a wind sensor. The method also includes providing a plumeof the target chemical, the plume may include a horizontal distributiondeviation defined as a standard deviation of a horizontal distributionof a plume concentration. The method also includes a verticaldistribution deviation defined as a standard deviation of a verticaldistribution of the plume concentration. The method also includesidentifying an emission rate of the target chemical at the source usinga plume model functionally operated by a standard deviation ofhorizontal distribution. The method also includes a standard deviationof vertical distribution. The method also includes the concentration atthe predominate air quality monitor. The method also includes the windspeed. The method also includes further identifying the source from aplurality of possible sources of the target chemical by correlating theemission rate. The method also includes outputting the identified sourceto a computer device. The method also includes providing a cloud server.The method also includes where the predominate air quality monitorfurther may include a processor. The method also includes an averagingroutine operatively associated with the processor. The method alsoincludes averaging a series of the first concentration of the targetchemical to generate an averaged first concentration, according to atleast one of a) with the processor at the predominate air qualitymonitor, b) according to the averaging routine, and c) beforetransmitting the averaged first concentration to the cloud server. Themethod also includes after transmitting the first concentration to thecloud server, filtering a population of the first concentration toidentify cyclical emissions that are dependent on at least one of a timeof day and/or a day of month. The method also includes a month of year.The method also includes temperature. The method also includes winddirection. Other embodiments of this aspect include correspondingcomputer systems, apparatus, and computer programs recorded on one ormore computer storage devices, each configured to perform the actions ofthe methods.

In yet another configuration, one general aspect includes acomputer-implemented method for identifying a source of a targetchemical. The computer-implemented method also includes providing atleast a predominate air quality monitor may include a first sensorresponsive to the target chemical. The method also includes a firstlocation at which the predominate air quality monitor is located. Themethod also includes measuring a first concentration of the targetchemical at the predominate air quality monitor as a function of a windspeed. The method also includes a wind direction. The method alsoincludes where the wind speed and the wind direction are measured usinga wind sensor. The method also includes providing a plume of the targetchemical, the plume may include a horizontal distribution deviationdefined as a standard deviation of a horizontal distribution of a plumeconcentration. The method also includes a vertical distributiondeviation defined as a standard deviation of a vertical distribution ofthe plume concentration. The method also includes identifying anemission rate of the target chemical at the source using a plume modelfunctionally operated by a standard deviation of horizontaldistribution. The method also includes a standard deviation of verticaldistribution. The method also includes the concentration at thepredominate air quality monitor. The method also includes the windspeed. The method also includes further identifying the source from aplurality of possible sources of the target chemical by correlating theemission rate. The method also includes outputting the identified sourceto a computer device. The method also includes providing a cloud server.The method also includes transmitting a population of the firstconcentration to the cloud server. The method also includes identifyinga highest first concentration of the population of the firstconcentration. The method also includes identifying a lowest firstconcentration of the population of the first concentration. The methodalso includes determining a signal-to-noise threshold. The method alsoincludes dividing the first concentration by a difference between thehighest first concentration and the lowest first concentration toproduce a signal-to-noise ratio. The method also includes discardingindividual readings of the first concentration that has asignal-to-noise ratio below the signal-to-noise threshold. Otherembodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

In another configuration, one general aspect includes acomputer-implemented method for identifying a source of a targetchemical. The computer-implemented method also includes providing atleast a predominate air quality monitor may include a first sensorresponsive to the target chemical. The method also includes a firstlocation at which the predominate air quality monitor is located. Themethod also includes measuring a first concentration of the targetchemical at the predominate air quality monitor as a function of a windspeed and/or a wind direction. The method also includes where the windspeed and the wind direction are measured using a wind sensor. Themethod also includes providing a plume of the target chemical, the plumemay include a horizontal distribution deviation defined as a standarddeviation of a horizontal distribution of a plume concentration. Themethod also includes a vertical distribution deviation defined as astandard deviation of a vertical distribution of the plumeconcentration. The method also includes identifying an emission rate ofthe target chemical at the source using a plume model functionallyoperated by a standard deviation of horizontal distribution. The methodalso includes a standard deviation of vertical distribution. The methodalso includes the concentration at the predominate air quality monitor.The method also includes the wind speed. The method also includesfurther identifying the source from a plurality of possible sources ofthe target chemical by correlating the emission rate. The method alsoincludes outputting the identified source to a computer device. Themethod also includes providing a cloud server. The method also includeswhere the predominate air quality monitor further may include aprocessor. The method also includes an averaging routine operativelyassociated with the processor. The method also includes averaging aseries of the first concentration of the target chemical to generate anaveraged first concentration, according to at least one of a) with theprocessor at the predominate air quality monitor, b) according to theaveraging routine, and c) before transmitting the averaged firstconcentration to the cloud server. The method also includes theaveraging to generate the averaged first concentration is dependent oneither the wind speed or the wind direction. The method also includesthe averaging to generate the averaged first concentration is increasedwhen either the wind speed decreases below a diffusion-only speed or.the wind direction indicates delivery of dry air to the predominate airquality monitor.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various configuration, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures of the drawing, which are included to provide afurther understanding of general aspects of the system/method, areincorporated in and constitute a part of this specification. Theseillustrative aspects of the system/method, and together with thedetailed description, explain the principles of the system. No attemptis made to show structural details in more detail than is necessary fora fundamental understanding of the system and various ways in which itis practiced. The following figures of the drawing include:

FIG. 1 illustrates an example of an air quality monitoring system, inaccordance with an illustrative configuration of the present disclosure;

FIG. 2 illustrates an example air quality monitor and select examplecomponents that may be included, in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 3 illustrates a flow chart of an example cross-calibration methodfor calibrating a gaseous chemical sensor in an air quality monitor, inaccordance with an illustrative configuration of the present disclosure;

FIG. 4A illustrates an overview of sensor calibration, in accordancewith an illustrative configuration of the present disclosure;

FIG. 4B illustrates an overview of sensor calibration update, inaccordance with an illustrative configuration of the present disclosure;

FIG. 5 illustrates a flow chart of an example source determinationmethod, in accordance with an illustrative configuration of the presentdisclosure;

FIG. 6 illustrates an example of a gaseous chemical sensor with examplecomponents, in accordance with an illustrative configuration of thepresent disclosure;

FIG. 7A illustrates an embodiment of the sensor system, which isdeployed in the field, in accordance with an illustrative configurationof the present disclosure;

FIG. 7B illustrates an embodiment of a communication architecture of aset of sensor systems, in accordance with an illustrative configurationof the present disclosure;

FIG. 7C illustrates a symbolic map representation of a sensor deploymentamid the field where sources are present, in accordance with anillustrative configuration of the present disclosure;

FIG. 7D illustrates another view of the embodiment of the sensor systemof FIG. 7A, in accordance with an illustrative configuration of thepresent disclosure;

FIG. 8A illustrates an embodiment of a method for compound measurementrelated to spectroscopy, in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 8B illustrates an embodiment of a method for converting messagesfrom the sensor systems via cloud implementation, in accordance with anillustrative configuration of the present disclosure;

FIGS. 8C-8D illustrate a front view and a top view, respectively, of anexample site that includes an emission source, in accordance with anillustrative configuration of the present disclosure;

FIG. 8E illustrates a top view of a scenario with respect to an examplesite that includes multiple emission sources, in accordance with anillustrative configuration of the present disclosure;

FIG. 9 illustrates a simplified graphical explanation of the physicsinvolved in a specific embodiment of the sensing technology; namelyabsorption spectrophotometry, in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 10 illustrates a detailed description of the process involved inthe interpretation of the spectra obtained from the embodiment of thesensing technology presented in FIG. 9 , in accordance with anillustrative configuration of the present disclosure;

FIGS. 11A-11D illustrate multiple cases of the transport of a compoundfrom a source to a sensor, based on wind direction, wind speed, anddynamic wind effect, in accordance with an illustrative configuration ofthe present disclosure;

FIG. 11E illustrates a graph related to the concentration across thecross section of an emission plume, in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 12 illustrates an embodiment of a method to quantify, qualify andlocalize sources relying on transport simulation and a sourceidentification strategy relying on solving an inverse problem, inaccordance with an illustrative configuration of the present disclosure;

FIGS. 13A-13B illustrates another embodiment of a method to quantify,qualify and localize sources, in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 14A illustrates an embodiment of an operational flowchart for thequalification of emission type using statistical inference, inaccordance with an illustrative configuration of the present disclosure;

FIG. 14B illustrates a graph related to the number of observations andaverage emission rates, in accordance with an illustrative configurationof the present disclosure;

FIGS. 15A-15D illustrates a set of figures representing the effect ofterrain, usual wind pattern and source separation on the detection areaof a sensor deployed in the field, in accordance with an illustrativeconfiguration of the present disclosure;

FIGS. 16A-16B show two symbolic maps of sensor network deployments for adiffuse source area and for point sources, respectively, in accordancewith an illustrative configuration of the present disclosure;

FIG. 17 illustrates an example Gaussian plume model that includes aplume modeled as radially extending with horizontal and verticalspreading, in accordance with an illustrative configuration of thepresent disclosure;

FIG. 18 illustrates a graphical representation illustrating radialdistance and angle between a source and a detector, in accordance withan illustrative configuration of the present disclosure;

FIG. 19 illustrates an example of analysis performing localization of asite (e.g., Colorado State University's METEC Lab experimental site)with the probability curves given as a function of wind direction, inaccordance with an illustrative configuration of the present disclosure;

FIGS. 20A-20B illustrate graphical representations illustrating exampleof five events detected along with background concentration, inaccordance with an illustrative configuration of the present disclosure;

FIG. 21 illustrates a graphical representation of cumulative predictiveemissions for a site (METEC Site Emissions) over the course of threedays as compared to true emissions, in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 22 illustrates a workflow diagram showing a framework ofquantification, in accordance with an illustrative configuration of thepresent disclosure;

FIG. 23 illustrates a graphical representation depicting a configurationof a dashboard—time-series concentration, in accordance with anillustrative configuration of the present disclosure;

FIG. 24 illustrates an example wind rose diagram defined by a weatherdata for a site (e.g., METEC site), in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 25 illustrates another example wind rose diagram including apredominate wind direction, a secondary wind direction, and a tertiarywind direction over a period of time (e.g., a year), in accordance withan illustrative configuration of the present disclosure;

FIGS. 26A-26G illustrate a wind rose diagram for each of a weekincluding a predominate wind direction (shown as “1”) during that day,in accordance with an illustrative configuration of the presentdisclosure;

FIG. 27 illustrates a graphical representation of exampleSignal-To-Noise (SNR) associated with different detectors beforeelimination of detectors, in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 28 illustrates a graphical representation of example SNR associatedwith different detectors after elimination of detectors, in accordancewith an illustrative configuration of the present disclosure;

FIG. 29 illustrates a representation of a process wind directionalfiltering of detectors which are not in the appropriate position of thewind with moderate dispersion, in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 30 illustrates a graphical representation of flux predictions forall emission sources based on predictive algorithm for the experimentsconducted at a site (e.g., METEC test site) over a cumulative period ofthree days, in accordance with an illustrative configuration of thepresent disclosure;

FIG. 31 illustrates a graphical representation of flux prediction errorfor all emission sources based on predictive algorithm for theexperiments conducted at a site (e.g., METEC test site) over acumulative period of three days, in accordance with an illustrativeconfiguration of the present disclosure;

FIG. 32 illustrates a schematic representation of time-seriesconcentration and wind speed for an example experiment study, inaccordance with an illustrative configuration of the present disclosure;

FIG. 33 illustrates a graphical representation of flux prediction error(%) as a function of wind speed (WS), wind direction (WD), upwinddetectors, downwind detectors during a test period at a site, inaccordance with an illustrative configuration of the present disclosure;

FIGS. 34A-34B illustrate graphical plotting of error bounds in aGaussian Plume Model and a Model S, respectively, in accordance with anillustrative configuration of the present disclosure;

FIG. 35 illustrates a plan of an example site under monitoring, inaccordance with an illustrative configuration of the present disclosure;

FIG. 36 illustrates another plan of an example site under monitoring, inaccordance with an illustrative configuration of the present disclosure;and

FIG. 37 illustrates a graphical representation of example SNRsassociated with different sensors deployed at a site, in accordance withan illustrative configuration of the present disclosure.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label. Where thereference label is used in the specification, the description isapplicable to any one of the similar components having the samereference label.

DETAILED DESCRIPTION

Illustrative configurations are described with reference to theaccompanying drawings. Wherever convenient, the same reference numbersare used throughout the drawings to refer to the same or like parts.While examples and features of disclosed principles are describedherein, modifications, adaptations, and other implementations arepossible without departing from the spirit and scope of the disclosedconfigurations. It is intended that the following detailed descriptionbe considered as exemplary only, with the true scope and spirit beingindicated by the following claims.

FIG. 1 shows an example of an air quality monitoring system 110, whichhandles air quality data from different sources. As illustrated in FIG.1 , air quality monitoring system 110 may include an air quality dataprocessing module 121, a plurality of air quality monitors 132-134,reference monitors 137 and environmental monitors 139. Air qualitymonitors 132-134 can include one or more chemical sensors configured todetect and measure chemicals, such as ozone, nitrogen oxide, carbondioxide, sulfur dioxide, volatile organic compounds, methane or otherhydrocarbons, and other chemicals in gaseous state (these are hereinbeing described as gaseous chemicals), as well as one or more particlesensors configured to detect and measure the presence of suspendedparticles in air such as dust, smoke, pollen, or soot (these are hereindescribed as particulate matter or PM). Air quality monitors 132-134 mayinclude an enhanced gaseous chemical sensor having a multi-pass cell forlight rays, as will be described in more detail below, such as inconjunction with FIG. 6 . Air quality monitors 132-134 may be located atmultiple different locations. For example, multiple monitors may belocated around a sizable area, such as a county, a city, or aneighborhood. Several instruments may also be located within a buildingor a dwelling.

Reference monitors 137 include precision gaseous chemical sensors andare configured to provide measurements for use in calibrating thegaseous chemical sensors in air quality monitors 132-134. Environmentalmonitors 139 are configured to measure environmental conditions, such ashumidity, temperature, atmospheric pressure, air density, ambient light,geographic location, wind speed and direction, and the like.

Air quality data processing module 121 is configured to communicate withair quality monitors 132-134, reference monitors 137, and environmentalmonitors 139. For example, air quality data processing module 121 mayreceive data from these monitors, such as measurements. Air quality dataprocessing module 121 may also transmit data to these monitors, such asproviding calibration data. Air quality data processing module 121 cancorrect measurements from air quality monitors 132-134 usingcross-calibration factors, as will be explained below. Air quality dataprocessing module 121 is also configured to process the data frommonitors and perform analyses to calculate or infer additional airquality data such as the amount of various gaseous chemicals in variouslocations, sources of those gaseous chemicals, and recommendations basedon elicited requirements or preferences of end users. Air quality dataprocessing module 121 is configured to communicate with mobile devices152, computing devices 151 and server devices 153 to receive data andprovide received, calculated, and inferred air quality data. Forexample, air quality data processing module 121 may receive user-inputdata and use that data to derive additional air quality data relevant tothe area of analysis. Air quality data processing module 121 is alsoconfigured to communicate with other sources of data such as reportingsystem 154 and weather stations 155. Air quality data processing module121 may be implemented in any appropriate physical or virtual computingplatform (such as a networked server) and may operate and act throughany suitable interface (such as a cloud computing platform).

Air quality monitoring system 110 may also be configured to processincoming data to provide a variety of outputs. For example, air qualitymonitoring system 110 may analyze measurements from air quality monitors132-134 to determine sources of the gaseous chemicals being detected.Air quality monitoring system 110 may provide actionable steps to affectthe chemical sources, such as ways to reduce the release of thosechemicals or ways to minimize exposure to those chemicals, making use ofstated preferences or user requirements, and/or ancillary (e.g.,topological, geological, meteorological, demographic) datasets relevantto the area of investigation. The air quality monitoring system 110 canbe used to quantify, qualify and/or localize sources, as discussed inconnection with FIGS. 12-16 .

FIG. 2 shows an example air quality monitor 200 (such as air qualitymonitors 132-134 in FIG. 1 ) and some example components that may beincluded therein. Air quality monitor 200 may include processing module211, memory 213, communication module 215, and one or more gaseouschemical sensors, such as chemical sensors 221-223, and environmentalsensor 230. Processing module 211 processes computing tasks and controlsother components. The computing tasks may include calibration. Memory213 stores data, such as measurement data from gaseous chemical sensors221-223 and calibration data such as cross-calibration factors. Chemicalsensors 221-223 are configured to measure gaseous chemicals andparticulates in analyte gas, such as gas under sampling by air qualitymonitor 200. Environmental sensor 230 measures environment conditions,such as temperature, pressure, humidity, location, wind speed, and thelike. Communication module 215 handles communication with other devices.For example, communication module 215 may handle communication betweenair quality monitor 200 and air quality data processing module 121 ofFIG. 1 , other air quality monitors, user-devices such as mobile devices152 and computing devices 151 and 153, and the like. Communicationmodule 215 may communicate through any of a variety of wired andwireless mechanisms, such as Wi-Fi, Bluetooth, mobile networks,long-range radio, satellite, and the like. Air quality monitor 200 mayalso be configured to measure time, position, and other relevantinformation for computing devices. The components, functionality, andconfiguration of the sensor can be selected based on desired monitoringcapabilities.

Sensor Calibration Process: Air quality monitoring system 110 of FIG. 1may be configured to increase the accuracy of low-precision gaseouschemical sensors through cross-calibration. Operators of air qualitymonitoring system 110 may implement a cross-calibration method 300 asshown in FIG. 3 . This cross-calibration method 300 can improve theaccuracy of low-precision gaseous chemical sensors, which are sensitiveto both their target gas as well as additional parameters, including(but not limited to) other gases, changes in environmental conditions(wind, pressure, humidity/moisture), or e.g., radio waves.Cross-calibration method 300 calculates sensitivity of one of thegaseous sensors to the target gas as well as confounding factors anddeduces the true value of the target gas by, for example, placing airquality monitors with low-precision gaseous chemical sensors next to areference monitor with high-precision sensors.

Example equations for calculating cross-calibration factors and errorsfor calibration are shown below. According to the cross-calibrationmethod, a low-precision sensor tasked with measuring a gas concentrationX is sensitive to additional parameters y₁, y₂, . . . , y_(n), asillustrated in equation 1. In practice, one or more air quality monitorsand one or more reference monitors may be used. The air quality monitorswith low-precision gas sensors are placed next to reference monitorswith high precision gas sensors which are not sensitive to theseadditional parameters. The calibration method determines theconcentration of gas X as a function of the measured concentration y₀and additional parameters y₁, y₂, . . . , y_(n) using the followingequation. Coefficients a₀, a₁ . . . , a_(n) are determined where thesecoefficients represent sensitivity of the low precision sensor toparameter y₁, y₂, . . . , y_(n).y ₀ =a ₀ X+a ₁ y ₁ +a ₂ y ₂ + . . . +a _(n) y _(n)

After the air quality monitors are deployed in the network, an airquality monitor with low-precision gaseous chemical sensors may beplaced next to a reference monitor with high-precision gaseous chemicalsensors. The error between the high-precision monitor and thelow-precision monitor may be calculated using the following equation:

$\epsilon = {X^{\prime} - {\frac{1}{a_{0}}\left( {y_{0} - {a_{1}y_{1}} - \ldots - {a_{n}y_{n}}} \right)}}$

If the error is nonzero, the cross-calibration method is performed forthis pair of low-precision sensor and reference sensor. The differencein original and updated parameters y₁, . . . , y_(n) is reported andthen pushed to devices on the network. For a naive implementation, onlythe error E is applied as a correction to the network of air qualitymonitors with similar low-precision gaseous chemical sensors, thoughmore involved methods may be used.

In the air quality monitor network, cross-calibration method 300 can beimplemented by first placing each of the air quality monitors next to areference monitor to calculate coefficients of the parameters forcalibration. As described in step 312, and illustrated in FIG. 4A,cross-calibration begins by co-locating low-precision gaseous chemicalsensors and high-precision gaseous chemical sensors. These sensors canbe co-located using any of a variety of different configurations, suchas by themselves, while incorporated in air quality monitors andreference monitors, a mix of different configurations, and the like.

At step 314, cross-calibration factors are determined. Cross-calibrationfactors may include coefficients for parameters associated with thelow-precision gaseous chemical sensors. These cross-calibration factorsmay be implemented in any of a variety of ways and data structures, suchas being simple values of the coefficients, combining the coefficientswith the parameters, as an array of values, and the like. Thesecross-calibration factors may be used by individual air qualitymonitors, air quality data processing modules, other systems tocalibrate and correct measurements of low-precision sensors of airquality monitors, and the like.

At step 316, the air quality monitors are deployed in the network. Asshown in FIG. 4B, one or more selected low-precision gas sensors 433 maybe kept in proximity to one or more reference monitors 421 for updatingcross-calibration after deployment.

Cross-calibration method 300 of FIG. 3 can calculate updatedcoefficients in real-time and apply that update to the network of airquality monitors. For example, at step 318, an error is computed todetermine if the cross-calibration factors require an update. The errormay be calculated using example equation 2 and as discussed above.

At decision step 321 of FIG. 3 , a determination is made whether theerror exceeds a threshold. An error that exceeds a threshold indicatesthat the cross-calibration factors may require an update. If the errorexceeds the threshold and an update is needed, method 300 moves to step332 where the cross-calibration factors are updated based onmeasurements from the low-precision gaseous chemical sensor andhigh-precision gaseous chemical sensor that were co-located afterdeployment for calibration updating purpose, as discussed in connectionwith step 316 and FIG. 4B. At step 341, the updated cross-calibrationfactors are provided to the air quality monitors of the network and theprocess ends.

Back at decision step 321, if the error is below the threshold, method300 moves to step 333 where cross-calibration factors of air qualitymonitors of the network are up to date and the process ends.

Cross-calibration method 300 above may be implemented in any of avariety of different ways by different devices in many combinations. Forexample, air quality data processing modules may implement this processto initially calibrate the air quality monitors by determining thecross-calibration factors and then updating the air quality monitors ona periodic basis. The air quality data processing module may correctdata received from the deployed air quality monitors based on theupdated cross-calibration factors, push these updated factors to thedeployed air quality monitors so that the monitors can update the databefore sending these data to the air quality data processing module andother devices, a combination of correction steps, and the like.

One or more steps of the cross-calibration method 300 can be used tocalibrate other types of sensors. Output from non-gas sensors can beused to further calibrate the gas sensors. For example, the method 300can be used to calibrate wind sensors by, for example, using windsensors rather than gas sensors. High-precision wind sensors and lowprecisions sensors can be employed. The cross-calibration method 300 canbe modified based on the sensor characteristics and desired level ofprecision.

Gas Sensors Calibration Examples: The below examples illustrate somepossible implementation scenarios of the calibration process and examplecapabilities of the air quality monitoring system.

Calibration Example 1: A low-precision ozone sensor is sensitive torapid changes in humidity and nitrogen dioxide. Using a high-precisioninstrument, the process calculates sensitivity of the sensor to ozone,humidity changes, and nitrogen dioxide. The process uses these values toeliminate humidity changes and nitrogen dioxide from the values returnedby the low-precision ozone sensor and deduce the true ozone value.

Calibration Example 2: A low-precision sulfur dioxide sensor issensitive to changes in humidity and passing radio waves. The systemcombines the sulfur dioxide sensor with a humidity sensor, and ahydrogen sulfide sensor with similar sensitivity to confoundingfactors—while hydrogen sulfide (outside of sewers and marshes) is knownto be low in the environment where the sensor system is deployed. Theprocess calculates the sensitivity of the hydrogen sulfide sensor tosulfur dioxide and the sensitivity of the sulfur dioxide sensor tohumidity changes. When the sulfur dioxide sensor reads high and hydrogensulfide reads high, the system ignores the sulfur dioxide reading,assuming that a passing radio wave is setting off the system. When thesulfur dioxide sensor reads high and hydrogen sulfide sensor reads low,then a sulfur dioxide reading is confirmed.

Calibration Example 3: A network of air quality monitors is installed ina city. One of the air quality monitors is placed next to a referencemonitor with high-precision gas sensors. Periodically (e.g., everyminute), the error between the air quality monitor and the referencemonitor is calculated and applied as a correction to other air qualitymonitors in the network.

Source Determination and Action Recommendation Process: Air qualitymonitoring system 110 can be configured to determine sources of gas thatare detected by air quality monitors. An example source determinationmethod 500 is shown in FIG. 5 . At step 516, user-input data related toan environment is determined. The user-input data may include any typeof input about the environment, as such conditions associated with oneor more air quality monitors deployed around a location. User-input datacan include any of a variety of types of data such as:

-   -   1. type and location of objects, such as newly installed carpet        that can out-gas chemicals;    -   2. events that can cause chemical emissions in the air, such as        cleaning using chemical products;    -   3. layout of the location of concern, such as the placement of        vents, windows, and doors; and    -   4. users' personal data, such as allergies, medical conditions,        health concerns, daily routines, travel plans, and the like.

Air quality monitoring system (e.g., air quality monitoring system 110of FIG. 1 ) may receive this data in any of a variety of way, such asthrough a website, an application installed on a mobile device,automatically from home sensors or mobile devices, information fromother systems and services, and the like.

At step 524, the source determination method 500 determines measurementsfor a target gaseous chemical over a period. These measurements may beprovided by air quality monitors of air quality monitoring system 110.At step 528, additional data associated with the measurements aredetermined. The additional data may include data from a variety ofsources that are relevant to determining sources of gaseous chemicalsmeasured by air quality monitoring system 110. The additional data maycome from any of a variety of sources, such as weather data from weatherstations, traffic data from traffic management administration, chemicalemission events data such as from government reporting agencies, onlineservices such as social networks, and the like.

At step 532, a plurality of possible sources of the target chemical areidentified based at least in part on the user-input data and additionaldata. At step 536, one or more sources from the plurality of possiblesources of the target chemical are identified by correlation. Thecorrelation may be determined between the measured data from an airquality monitor, user-input data, and additional data. For example, thepresence and amount of a gaseous chemical may correlate to an event oran object at the proximate location and at around the same time. Thecorrelation process may be implemented in any of a variety of ways. Anexample process is shown below along with example equations thatillustrate the methodology. Artificial intelligence algorithms andcloud-based data analytics may be employed as part of the correlationprocess.

At step 544, at least one determined source is output. The source may beoutput in many different ways, such as data to a service, a website, auser-interface on a mobile app, and the like. Source determinationmethod 500 may also provide recommendations, such as to reduce thegaseous chemical from the source, reduce exposure to the gaseouschemical, and the like. Examples are provided in the sourcedetermination examples below.

Correlation Process and Calculations: An example of correlation stepsand calculations are provided below:

-   -   1) Sort training data into categories using a clustering        algorithm, such as a k-means clustering approach. Given a set of        d parameters and n observations of each parameter, the present        disclosure solved the following minimization equation to cluster        the data into k sets S. This is done by finding means mu (μ):

$\begin{matrix}{{\underset{S}{\arg\min}{\sum}_{i = 1}^{k}{\sum}_{x \in S_{i}}{{x - \mu}}^{2}} = {\underset{S}{\arg\min}{\sum}_{i = 1}^{k}{❘S_{i}❘}\sigma^{2}S_{i}}} & {{Equation}3}\end{matrix}$

-   -   2) In real-time, feed in data. Using the categorization        established in (1), determine which category Si variable x is        most likely to fit by solving for i:

$\begin{matrix}{\min{\sum}_{i = 1}^{k}{\sum}_{x \in S_{i}}{{x - \mu_{i}}}^{2}} & {{Equation}4}\end{matrix}$

-   -   3) Map the categorization S to solutions S′ using scientific        literature reviews, best practices from experts, and clinical        guidelines.

Source Determination and Action Recommendation Examples: The belowexamples illustrate some possible implementation scenarios of thechemical source differentiation process and example capabilities of theair quality monitoring system.

Source Determination and Action Recommendation Example 1: A volatileorganic compound (VOC) sensor detects a large, quick increase in VOCconcentration that quickly dissipates. By considering the concentration,change of concentration over time, and time of the signal, the processdetermines that the source is most likely to be a consumer cleaningproduct.

Source Determination and Action Recommendation Example 2: Detecting highVOC concentrations in an indoor environment, the air quality monitoringsystem recommends that individuals open a window to increase airflow andreduce their exposure.

Source Determination and Action Recommendation Example 3: The airquality monitoring system detects high temperature, pressure, and ozonelevels outdoors characteristic of a stationary pressure weather systemduring the summertime on the East Coast. The system determines that thehigh ozone levels are most likely due to high levels of ozone beingblown into the area, coupled with high levels of traffic. The systemrecommends that the city increase carpooling and public transportationuse.

Source Determination and Action Recommendation Example 4: The airquality monitoring system detects moisture, pressure, and high levels ofparticulate matter during an early fall cold spell in the PacificNorthwest. It deduces that an inversion layer is responsible for thebuildup in pollution and suggests that the city reduce biomass burningto reduce pollution (e.g., what is colloquially referred to as a ‘burnban’).

Source Determination and Action Recommendation Example 5: The airquality monitoring system detects high levels of particles and nitrogendioxide in India in the winter. The system recommends that users wear aprotective mask to lower their health exposure to pollution.

Enhanced Gaseous Sensor with Paired Spectrophotometry and Nephelometry:The air quality monitoring system described herein may include anenhanced gaseous chemical sensor configured as a low-maintenancespectrophotometer and nephelometer that identifies gaseous chemicals bytheir light absorption spectrum and particulate matter by theirscattering spectrum. The gaseous chemical sensor described below is agaseous chemical sensing device for measuring chemicals in air. Itincludes a light source that emits light rays, and a spectrophotometricdetector. The chemical sensor also includes a cell having two reflectivesurfaces located at opposite ends of the cell. The reflective surfacesare configured to reflect the light rays along a path across the celland to direct the light rays to the spectrophotometric detector. Thiscell enables light rays to pass through the analyte gas multiple timesto enable more accurate measurements and to minimize the interference ofparticulate matter in the analyte gas with respect to the spectralanalysis. The configuration of the cell also enables the measurement ofparticulate matter in the analyte gas, for example, a sensor thatmeasures light scattered by particulate matter that intercepts the lightrays along the path.

The chemical sensor also includes an analyzer that measures at least onechemical by receiving measurements made by the spectrophotometricdetector. It is configured to determine the amount of spectralabsorption due to presence of at least one gaseous chemical and tocompensate for the presence of particulate matter based on the amount ofscattered light measured by the photodetector. An example such sensorthat can be implemented in an air quality monitor and example componentsof the sensor are illustrated in FIG. 6 and described in more detailbelow. The example components illustrated in FIG. 6 include:

-   -   I: Gas intake followed by an inertial trap for large particulate        matter    -   O: Gas outlet (where the analyte is pumped out)    -   S1: light source    -   A1, A2, A3: Round apertures to improve coherence    -   L1, L2, L3: lenses for the collimator/concentrator system; L3        serves as seal    -   M1: Alignment mirror to inject the collimated rays in the        multi-pass mirror cell    -   M2, M3: pair of flat or concave mirrors forming a multi-pass        mirror cell for path length extension.    -   W1, W2: Observation windows for the spectrometers    -   SP1, SP2: Spectrophotometer sensors; SP2 acts alternately as a        nephelometer

Optical Description: Light is produced by the light source S1 andcollimated into (nearly) parallel rays by the optical system formed byL1, L2, L3, A1 and A2. The light source may have a reflector to improvelight concentration. The chosen layout of lenses and aperturesconcentrate the collimated light into a tighter beam as well. Any othersystem of optical elements may be used to generate the light collimationand concentration, or other sources of collimated light such as a laseror laser comb may be employed.

The rays of light, concentrated and collimated, are injected in amulti-pass mirror cell by mirror M1. Such a cell may use flat orfocusing mirrors M2, M3 such as convex mirrors (as with a Herriott cell,White cell, Pfund cell or circular multi-pass cell). The cell isconfigured to increase the pathlength of light passing through theanalyte gas. The longer path length increases absorption andSignal-to-Noise Ratio (SNR). The rays of light, after exiting the cell,are directed toward the spectrophotometer sensor SP1. This sensor andaccompanying digital circuitry determine the intensity and spectraldistribution of light that travelled from the source and through theanalyte gas. Sensor SP2 analyzes the spectrum and intensity of lightrays scattered by any particulate matter present in the analyte gastraveling through the system from I to O. Micro-spectrophotometers, suchas a Fabry-Perot Interferometer (FPI, such as those made by themanufacturer Infratec®) or diffraction grating-basedmicro-spectrophotometers (such as those made by manufacturer Hamamatsu)among others, may be suitable for SP1 and SP2. Alternatively, the lightsource S1 may be tuned to span the wavelengths of interest.

Structural Description: The body of the device separates the optics andelectronics from the environment, exposes the optical cell to the pumpedgas analyte, rigidly maintains alignment of the optical elements, andlimits the influence of stray light rays on the light spectrum emittedby S1 and on the measurements obtained by SP1 and SP2. The body of thedevice may be built as a single structural unit with top and bottomplates for sealing. The body may be built out of various rigidmaterials, and can be 3d-printed, machined, molded, injected, extruded,or produced through other suitable processes. Interface elements areused to connect the optical elements to the body. The structure may alsobe optimized to limit the upper bound of particulate matter in thedevice body without placing filters on the input analyte gas channel.

For sealing, gaskets may be used between the mirror cell (exposed to theanalyte gas), the environment, and the optics and electronics(maintained in a dry and neutral atmosphere). Certain optical elements,such as the observation windows W1 and W2 and the lens L3, serve as aninterface between the optical chamber and the rest of the system, andmay be used as seals.

For optics alignment, the structure is rigidly constructed and may usesymmetries, topology optimization and low thermal expansion materials tolimit misalignment of the optical elements during the lifetime of thedevice. Opto-mechanics systems may be used to ensure calibration andpositioning of optical components. Interface elements in the form ofadjustable mounting plates act as optical holders for precise alignmentand calibration. These plates may be machined to fit the opticalelements precisely and may be linked to the main body with screws,bolts, or other fastening systems. The plates are set in place in thebody such that the described optical path is realized, and theirpositions can be calibrated at assembly and during periodic maintenance.

For stray light ray limitation, the entire body of the device in theoptical cell may be coated with a light-absorbing coating with lowreflectivity in the band of observational interest. This can be achievedin a variety of ways, such as by anodization, by deposition, or bypainting a coating using carbon black, nigrosin, black oxides of variousmetals (such as aluminum, zinc, or platinum), graphene, graphite, carbonnanotubes or fluorenes, felt, or various other materials. The structuremay further integrate optical baffles and reflection traps that canlimit stray rays reaching SP1 or SP2.

For passive, filter-less avoidance of clogs, an inertial trap can beused by taking advantage of the conservation of linear or angularmomentum. Vortex-like traps can be used to force larger particulates outfor high-speed pumping rate, and meander-like traps (such as thephysical element proximal to I in the schematic) can be used with alow-speed pumping rate. These structures can be added to serve aspassive filters of large particulate matter in order to extendoperations of the device between routine maintenance servicing.Disposable filters may also be used.

Functional Description of Gas Identification: The analyte gas may bepumped into the spectrometer through I and may exit the device throughO. Pumping can be performed with any type of fan or pump. The pump maybe located on the output line to provide laminar flow and pumps the gasout from O.

The analyte gas contains trace gases to be identified by thespectrometer. While passing in the spectrometer, the analyte gasintercepts rays of light and absorbs specific wavelengths, which dependupon the type and concentration of gases in the sample. Further, theanalyte gas may contain a heterogeneous suspension of particulate matterwhich intercepts, absorbs, and scatters light. The scattering dependsupon the size distribution of the individual particles in theparticulate matter suspension and the absorbance depends upon the albedoand geometry of those various individual particles. By observing thespectrum of the transmitted light and the spectrum of the scatteredlight, information relevant to the gas type and scattering profile ofthe particulate matter can be inferred.

Signal Processing: The raw information gathered by the proposed systemis in the form of light intensity, with respect to wavelength and time,as gathered from the spectrometers SP1 and SP2. These signals, calledspectra, depend on the properties of the analyte gas and particulatematter, the light source, the optical system, and the spectrometersensor properties. By using the known properties of the light source andthe optical system, the signal is first processed. The opticalproperties of the specific spectrometer sensors employed are also usedto further refine the signal. For the case of Fabry-Perot Interferometer(FPI) sensors, the information about the FPI transfer function is usedto apply a signal deconvolution (or convolution of the referencesolution) and enhance the sensor sensitivity when compared to themanufacturer-reported resolution.

For trace gas detection, the spectrum values from sensor SP2 is used tofurther enhance the spectrum data obtained by sensor SP1. Some of thespectrum of SP1 that may be attributed to absorption may in fact be theresult of scattering, and the scattered light spectrum obtained by SP2can be used to assist interpretation of the signal from SP1. Ad hocknowledge about the probable analyte content using the data analyticsmethods described herein, together with the known typical spectralsignatures for various gases at ambient environmental concentrations maybe used to assist with interpretation of the spectrometric data intoprobable gas mixtures and concentrations.

For particulate matter detection, the spectrum values from SP2 are usedto identify characteristics of the particulate, such as material, size,or albedo, depending on what is known or may reasonably be inferredabout a particular particulate matter suspension. The particulate matterscattering spectrum can be used as a fingerprint for a particular typeof particulate. The alignment of sensor SP2 in the optical system isstandard for a light-scattering detector known as a nephelometer.However, nephelometers generally do not use broad-spectrum analysis andrely on aggregate particle scattering behavior in a single lightwavelength to analyze scattered light. By using a broadband spectrum forlight source S1, more information could be gained on the size, material,or albedo of the particulate matter. Acquiring a library of spectralresponses for various particulate mixtures could be used to helpidentify probable mixtures of gases and particulate matter at ambientenvironmental concentrations in the sample, particularly when pairedwith environmental information regarding a given sampling location.

Example of Signal Processing for Methane Monitoring: The analyte gasmixture contains an inert gas, water vapor, carbon dioxide and methaneas well as particulate matter. The spectra from SP1 and SP2 arecollected, as well as the temperature and pressure of the chamber. Thesignals SP1 and SP2 are first deconvolved from the known transferfunction of the optical system and the light source at the knowntemperature and pressure of the chamber. The transmission spectrum fromSP1 is compensated by the scattered spectrum of SP2. Similarly, thetransmission spectrum from SP1 is used to improve the scattered spectrumof SP2. The individual reference spectra at known concentrations ofwater vapor, carbon dioxide and methane may be recovered frominvestigations or from a public database. The known transfer function ofSP1 is applied as a convolution to the reference spectra of water vapor,carbon dioxide and methane. One illustrative algorithm generatessynthetic spectra of the water vapor, carbon dioxide and methane mixturefrom the convoluted reference spectra for various concentrations at themeasured temperature and pressure. By minimizing the difference of thegenerated synthetic spectra and the actual refined spectrum from SP1,best estimates of the actual concentrations of water, carbon dioxide andmethane are found.

FIG. 7A presents a particular embodiment of a sensor system 700 capableof measuring a target compound and one or more environmental parameters(e.g., weather conditions) in a collocated and contemporaneous manner.The compound measurement function of the sensory system of FIG. 7A isperformed by the compound sensor or sensors 710. These sensor(s) arepoint sensors, which means that their function is to measure aparticular physico-chemical property of the target compounds todistinguish them from background atmospheric composition (targetedcompounds include, but are not limited to: one or more gases andaerosols that are emitted by one or more industrial, anthropogenic, ornatural activities). In particular, one embodiment focuses onhydrocarbons and other greenhouse gases that absorb in the mid-IR regionof the electromagnetic (EM) spectrum, in particular wavelengths between1 um and 5 um. In one embodiment, compound sensor 710 is an absorptionspectrophotometer that can measure mid-infrared absorption in the 3 umto Sum range of the EM spectrum. Without loss of generality, compoundsensor 710 may comprise other sensor technologies that may be similarlyused for the measurement of target compounds.

In order to capture a sample for analysis, a sampling cane 714 may beused to pump an air sample at a specific height and avoid sampling waterin the case of precipitation or other foreign agents of large size. Thesample may be pumped and conditioned by a sample pumping andconditioning system 719. The system depicted 719 may include a pump forsampling the air for the compound sensor 710, a filter for the removalof particulate matter and a coalescent filter for the removal of water.The system may further include desiccant filters, temperature andpressure adjustment systems, valves, and additional drain pumps tofacilitate moisture removal, temperature conditioning of the sample, orfor flushing and other filter regeneration tasks. The purpose of this isto provide a properly conditioned sample based on the sensor systemrequirements, while limiting the necessary maintenance of the pumpingand conditioning system 719.

In some embodiments, the compound sensor 710 may use an open path inorder to avoid the necessity of pumping or conditioning samples. Thesample may then be naturally transported into the sensing area byweather patterns without the use of a cane 714 or sampling pumping andconditioning system 719.

The sensor system of FIG. 7A further includes a weather sensor system711 collocated with the sampling point of the compound sensor 710 aroundthe sampling cane 714. The weather sensor system should at least includesensing elements to measure wind speed and direction. Further sensingabout temperature, pressure, hygrometry, insolation, and precipitationmay also be used to refine the subsequent modeling effort. The windspeed and direction may be measured by a combination of a wind vane andan anemometer, or by an anemometer alone such as in the case of using anultrasonic anemometer. The wind direction measurement may be made in twoor three dimensions. Temperature may be measured using MEMS sensors,thermistors, or other suitable sensing technology. Pressure may bemeasured using a barometer sensor and hygrometry by a moisture sensor.The sensors for temperature, pressure and moisture may be connected forimprovement of each of the measures as they are interdependent.Insolation may be measured using a photodiode or any other appropriatelight-sensitive sensor. Precipitation may be measured using aprecipitation sensor with auto-draining capability. While collocatingthe weather measurement with the sampling point is important for thepurpose of accurately characterizing emissions, it is not absolutelynecessary for performing the method as long as weather measurements arecollected in close proximity to the sensor system (e.g., within 100 m).This conformation, i.e., being collocated, minimizes the measurementerror and is the one illustrative configuration of the presentdisclosure.

The data collected by the compound sensor 710 and weather sensor system711 may be collected and processed by a local computing unit 717. Thelocal computing unit may also control the execution of the main samplingand measurement program and the actuation and controlling of anysubsystem of the sensor system 700. The local computing unit 717 runsthe main firmware, which schedules and collects data from compoundsensor 710 and weather sensor system 711, conditions the sensor signalsinto a rational format, performs data preprocessing, locally storesdata, formats, and prepares messages, and generates diagnostic andmetadata pertaining to the identification, time stamping and operationaldiagnostics of the sensor system and supporting circuitry. The messagesmay be encrypted and transferred to a communication unit 718 andmessages may be received from remote assets. The communication unit 718includes a modem or other interface that conditions the message to theright protocol for communication or receives external messages to becommunicated to the computing unit 717. The communication protocol maybe wired, such as a SCADA system or wireless, such as Bluetooth®, Wi-Fi,LoRa, cellular or satellite or any other radiofrequency, optical line ofsight, or other wireless data-transmission protocol. If a wirelessprotocol is employed, the data may be relayed using a communicationantenna 720, if appropriate. In general, a communication system, whichmay consist of a communication antenna 720 and communication unit 718,has a role that includes the communication of the measurement to aremote or centralized node and the receipt of communications related tosettings and operations changes or firmware updates. The communicationsystem may be used to relay messages to and from other sensor systemssuch as in a daisy chain, star, or mesh configuration in order to reducethe communication cost when relying on external communicationinfrastructure such as cellular or satellite communication networks. Incase of communication error, or other cases that warrant it, themessages may be stored by the computing unit 717 to communicate at alater more opportune time. For example, when communication services maybe interrupted, multiple channels of communication (such as multiplewireless data-transmission protocols) may be used to attempt to alertthe computing unit 717 to changes of operating conditions and to receiveinstructions.

The deployment of sensors in the field may require the exposure of theequipment to harsh outdoor conditions with no external support such aspower access and communication infrastructure. The sensing system ishoused in an enclosure 715 to protect the system from the environmentand from tampering. This may include, but is not limited to:precipitation, moisture, surface water and flooding, high temperatureand insolation, low temperatures, high winds, storms, hurricanes,typhoons, tornadoes, lightning, external impacts and vibrations,robbery, defacement, damage, earthquakes, light or electromagneticinterference, foreign agents or fauna and flora disturbance orintrusion. The enclosure 715 may also be highly visible by day andreflective at night to avoid accidental damage. The enclosure 715 may bedirectly on the ground, mounted on a foundation, or pole-mounted.

The sensor system in FIG. 7A may produce and manage its own power. Inone embodiment, the sensor system may include a solar power system 712and a power conversion and storage system 713. The solar power system712 and power conversion and storage system 713 are designed to providesufficient power to the various other subsystems with sufficientreserves and capacity to ensure proper functioning of the sensor systemin most environmental conditions present in the field. Solar powersystem 712 may be replaced by wind- or gas-based power generation, orany other form of compact power generation system if the conditionswarrant it. For instance, at high latitudes wind-based power generationmay be preferable to solar on account of low insolation. The powerconversion and storage system 713 may include a battery storage bank anda charge controller. The power conversion and storage system 713 mayfurther include power converters for providing appropriate power to thevarious systems, relays, fuses, and breakers, and switches appropriatefor the power protection, function, and physical interfacing required bya particular embodiment of the sensor system. The battery storage bankmay include lithium-ion (such as LiFePO4 cells), lead acid (such as adeep-cycle sealed battery) or any other appropriate battery technologythat can operate nominally in conditions that may include high and lowtemperatures and irregular charging profiles. The charge controller mayuse Pulse-Width Modulation (PWM) or Maximum Power Point Tracking (MPPT)or other technology appropriate to convert the raw energy from the solarpower system 712 to the battery storage bank charging requirements. Allsubsystems of FIG. 7A may be modular in nature to facilitate replacementof subsystems with minimal tools in the case of maintenance.

FIG. 7D shows another view 700D of the embodiment of the sensor systemof FIG. 7A. The system includes enclosure 715, anemometer 711, pole 716,sampling cane 714, and solar power system 712.

With regard to the sensor system disclosed in FIGS. 7A and 7D, certaincritical functions may be performed for the collection of sensor dataand for relaying sensor data through the communication units. Theflowchart 800A displayed in FIG. 8A presents an embodiment of a methodfor collecting weather data as well as compound measurement data. Inparticular, a compound sensor is capable of scanning the absorptionspectrum of a sample as presented in 814. Step 814 may be generalized toany other compound sensor system embodiments that are sensitive tocertain physical or chemical aspects of said compound(s) such thatconcentration of such compound(s) in the sample can be derived from themeasurements of such physical or chemical aspects with a sufficientactionable detection limit for the end user's intended application. FIG.8A presents an example of a method using a particular embodiment of thesensor. Other embodiments which collect and communicate compound andweather measurement may be also used. For example, the sensor system inFIGS. 7A and 7D may have other operational functions that can facilitatethe sensor system operation and the functions described in FIG. 8A.

The sensor system performs measurement of the weather concurrently tothe measurement of the compounds of interest. The weather measurementstep 819 by the sensors, such as those described in reference to FIG.7A, is performed continuously at a frequency f. In step 820, eachweather measurement is time-stamped at time p and saved. In step 823,the weather measurement at time p is stored in the internal memory. Thefrequency f may be read from an internal parameters table 821 and may bedynamically allocated. The measurement at time p in step 823 may also beobtained as a combination of multiple measurements obtained during step819. For example, wind direction may be measured every second but storedover 1 minute averages.

The sensor system of FIG. 8A as described above operates on a dynamicschedule for the sampling of air. In step 810 of FIG. 8A, the time stampt as kept and measured by the device and the scheduled start time s asread in from the internal parameters table 821 as stored in the internalmemory may be checked. The device compares times t and s in step 811 todetermine if it is time for starting the sample sequence. If too early(false; t<s), the device waits for the duration m in step 818 andrestarts the loop from step 810. The duration m may be selected as thetime difference between t and s minus the process time to loop from step818 to step 811. If step 811 instead finds that it is time to sample(true; t>=s), the function proceeds forward to step 812.

In step 812, initiation and readiness checks are performed. This mayinvolve diagnostic functions for all the subsystems, the communicationunit pinging the server, and readying of the compound sensor such asreaching a target temperature or any other necessary state foroperations. Step 812 may trigger the operation of a subsystem dedicatedto enforcing nominal conditions. For example, the temperature of thesensor may be found out of bounds for optimal operation and a thermalregulation subsystem may be triggered to raise or lower the sensortemperature. Step 812 may result in delaying the sensor measurement,aborting the sensor measurement in the case where critical issues arefound that inhibit measurement, delaying or aborting communication ofthe message if communication can't be performed, or fully aborting theperformance of the sampling sequence. For example, a battery voltage maybe found to be under a critical voltage that would reduce the sensorsystem's life expectancy between maintenance cycles, and the samplesequence may be aborted to avoid damaging the battery. Another examplemay be that the server link may not be possible at this time and themeasurement may be stored for subsequent communication. The diagnosticresult may be stored in step 812 for the purpose of communication to theserver and for storage in the internal logs for subsequent maintenancecheck. When all the diagnostic functions are performed in step 812 andif all the diagnostics point toward a nominal state, the firmware mayprogress to step 813.

In step 813, a sample acquisition mechanism may be triggered. In thecase of a short open path, the sample collection may be achievednaturally by the force of the wind without any actuator. Other systemsmay trigger a pumping mechanism that transfers the air sample to asampling chamber. The step 813 may further involve the trigger of activesubsystems for the conditioning of the sample, such as pneumatic systemsfor the removal of water or particulate matter or other undesirablecontaminants. For example, a subsystem may involve, prior to sampling, aregeneration mechanism for adsorption or absorption-based desiccation.The conditioning of the air sample in step 813 may be fully passive, forexample, when the pumping pressure differential is used for actuation inthe case of an auto-draining coalescing filter.

When the sample of air is in a sample cell where the compound- orcompounds-sensitive sensor operates, the measurement may be performed instep 814. The specific sensor technology embodiment presented in step814 of FIG. 8A may operate, for example, by scanning absorptionspectroscopy. In this case, the sensor measurement is operated byobserving sequentially a set of target wavelengths from j to k. For aspecific wavelength i, the sensor or source proceeds to shift in orderto observe the spectrum centered on wavelength i. The sensor's analogsignal is then measured for a time interval T and converted into adigital signal at a certain sampling rate. The digital signal is thenpreprocessed to identify the sensor's response intensity associated withthe measurement centered at wavelength i. This wavelength intensity isstored as part of the scan 822 of the sample measured at time stamp t,which is completed when all the intensities associated with wavelengthsj to k are measured.

When the sample scan is performed in step 814, the sensor systemproceeds to message preparation and formatting in step 815. The messageformatting involves message data 824 gathered from the internal memory.This may involve the current sample t measurement. This may include aset of weather measurements at time p measured before and duringsampling. Diagnostic information as well as sensor metadata identifyingthe sensor and its subsystem, operation, and such may also be added tothe message. Furthermore, previously captured, and stored sample andwind measurements may be added to the message data, for example whencommunication was unsuccessful at the previous sampling schedule time.Finally, relayed messages from other sensor systems deployed in thefield may be added to the message data 824, for example when the sensorsystems are networked to reduce the cost of communication to the centralcomputing unit. The formatting in step 815 may involve encryption of themessage. The message in step 815 may be further formatted into packetssuitable for transmission by the communication unit.

In step 816, the message is transmitted to the server in suitablepackets. Packet integrity may be evaluated to ensure that any datatransfer or communication failure may trigger retrying transmission orthe storage of the message for subsequent transmission. The sensorsystem may further query for an update of internal or operationalparameters in step 817. This step may involve a general firmware updatethat would alter the operation of the device to a new modality or maysimply influence critical parameters, such as the schedule for samplemeasurements, weather measurement frequency and storage or otherparameters that pertain to the operation of the critical andnon-critical functions. In some embodiments, step 817 may be triggeredby dynamically analyzing the latest measurements. For example, theschedule of subsequent measurements may be shifted as a response tochanges in recent past measurements. For instance, if a largeconcentration of a target compound is detected, the frequency ofmeasurements may be augmented to increase the response speed in case ofa critical emission. In another instance, wind measurement may triggeran immediate sample sequence in order to capture an emission from acritical direction. The critical direction may be, for example, thedirection from which a source emission is likely to be observed. Thisdynamic scheduling may be decided by a sensor system control unit usingedge computing resources, or by query from the centralized computingunit 727 of FIG. 7B for scheduling decisions requiring humanintervention or larger computing resources. The sensor system firmwaremay loop in step 817 for dynamic scheduling until the time for the nextscheduled sample approaches. The device may then proceed to step 818until time s is near and repeat the main sample loop starting at step810.

In some configurations, a cloud server (for example, “Amazon WesServices” or simply “AWS”) may be provided. Further, each of the sensorsystems (i.e., the plurality of air quality monitors) may includecontrol unit which may be using edge computing resources. The edgecomputing resources may further include a processor (for example,processing module 211), and an averaging routine operatively associatedwith the processor. The processor may be configured to average a seriesof the actual emissions measurements obtained by the each of theplurality of air quality monitors to generate an averaged actualemissions measurement. The averaged actual emissions measurement may begenerated by the processor at each of the plurality of air qualitymonitors, according to the averaging routine. The processor may befurther configured to transmit the averaged actual emissions measurementto the cloud server. It may be noted that the each of the plurality ofair quality monitors may be communicatively coupled to the cloud server.As such, the data received from all the plurality of air qualitymonitors may be received and analyzed at the cloud server.

In some configurations, the averaging to generate the averaged actualemissions measurement may be dependent on either the wind speed or thewind direction. Further, the averaging to generate the averaged actualemissions measurement may be increased when the wind speed decreasesbelow a diffusion-only speed. The diffusion-only speed may refer to thewind speed when the speed of wind is insufficient to cause considerablemovement of emission gases along with the wind. As such, the emissiongases only tend to diffuse in the surrounding air (i.e., move fromregion of high concentration to lower concentration). Additionally, oralternately, the averaging to generate the averaged actual emissionsmeasurement may be increased when the wind direction indicates deliveryof dry air (i.e., the air when the concentration of targetcompound/emission on the air is minimal or absent) to a predominate airquality monitor 3804(1) of the plurality of air quality monitors. Aswill be appreciated, increasing the averaging allows for more accuratedetection of emissions, if any, in the above situations.

Further, in some embodiments, the edge computing resource of the airquality monitor may include a memory (for example, memory 213). The airquality monitor may further include emissions sensors (for example,chemical sensors 221-223) configured to obtain sensor data at apredefined frequency. The memory may be configured to store sensor dataobtained by the emissions sensors. The air quality monitor may transmitthe sensor data to a cloud-base database (for example, “AWS”). Further,the edge computing resources (or processing module 211) may detect alow-connectivity condition. The low connectivity condition may be as aresult of network downtime/failure, power failure, etc. Upon detectingthe low-connectivity condition, the air quality monitor may startstoring the sensor data in the memory. Further, upon detecting anormal-connectivity condition, the air quality monitor may starttransmitting the sensor data stored in the memory to the cloud-baseddatabase.

Further, the air quality monitor may detect a threshold condition. Thethreshold condition may be one of a large concentration of a targetcompound, or a wind measurement from a threshold direction. It may benoted that the threshold direction may be a direction from which asource of emission is likely to be observed. In such a thresholdcondition, the air quality monitor may augment the frequency ofobtaining sensor data by the emissions sensor, based on the detection ofthe threshold condition. The processor (e.g., processing module 211) mayfurther procure the sensor data from the emissions sensor, and averagethe sensor data to obtain averaged data. The averaged data may beobtained according to one of a time-based criterion, or an event-basedcriteria. For example, the time-based criteria may define a time period(e.g., 60 seconds) after which the averaging of the sensors dataobtained during that period may be performed. The event-based criteriamay define an event (e.g., a low wind condition or high wind conditionbased on a windspeed threshold) on occurrence of which the averaging maybe performed. The air quality monitor may further include a transmittercommunicatively coupled to the processor. The transmitter may transmitthe averaged data to the cloud-based database. In some embodiments, theair quality monitor (i.e., the processor) may sequentially combine theaveraged of the sensor data into a data packet, and transmit theaveraged data to the cloud-based database via a receiver. The receivermay be one of a cellular networks, a wired network, a satellite, ashortwave radio, a CDMA network, or a GSM networks.

The embodiment of the system as in FIGS. 7A and 7D or any other sensorsystem embodiment capable of measuring target gas and weathermeasurements in a collocated manner may be deployed in a field whereprospective emission sources are present. A symbolic map 700C of aprospective field deployment is presented in FIG. 7C. In FIG. 7C, asensor system 741, as depicted by a rounded-corner square, is deployedin the field to detect emissions plumes 745, 750 of target compounds,depicted by color gradients. These emissions plumes 745, 750 may beemitted by point sources 742, 749 depicted by circles, or by area source743 depicted by a filled polygon. The plumes 745, 750 are transported byadvection by an air flow as denoted by streamline arrows 746, and bybuoyancy and diffusion of the compound in air. Typically, the air flowis of a complex three-dimensional geometry and depends on manyparameters including, but not limited to, terrain, surface roughness andobstacles, temperature and pressure differential, insolation andinversion layer position, turbulence, and atmospheric boundaryconditions or other atmospheric conditions forced by large-scale weatherpatterns. The streamlines 746 are a simplified view of the averagetransport (where turbulence is approached as an average) of air parcelsduring the sampling time. Note that the streamlines 746 are influencedby the effect of a terrain 747, as noted by isoclines, and by thepresence of obstacles 748 (e.g., trees) represented by the small blackdots. In this specific snapshot, the point source 742 is emitting thetarget gas, thereby producing plume 750 which is transported by the airflow 746 to the sensor system 741. Note that the cross section of theplume 750 increases when further from the source 742 due to diffusionand turbulent mixing. Plume 750 can also appear to have a tortuosity dueto the dynamic change in wind speed and direction during the transport.In this example, point source 749 is not emitting and area source 743 isemitting but its plume 745 does not intersect the position of the sensorsystem 741 in this particular snapshot. Note that plumes are typicallythree dimensional and may vary in vertical cross sections, though thisis not displayed in this figure.

It may therefore be necessary to have precise wind measurementcollocated at the sensor system as well as a modeling of the emissiontransport that considers terrain, obstacles, rugosity, and other fieldparameters that can affect transport. For instance, in the specificsnapshot presented in FIG. 7C, local wind pattern 751 at long distancecomes approximately from the East direction before entering the field ofinterest. The wind measurement collocated at sensor system 741 isapproximately Northeast as denoted with streamline 746 intersectingsensor system 741. From the perspective of sensor system 741, diffusingarea source 743 is located in the northeast sector, point source 742 islocated in the east-northeast sector, and point source 749 is in theeast sector. Only plume 750 from point source 742 is measured by sensor741 in this particular snapshot.

If a model only accounted for a wind direction and/or speed from a localweather pattern, such as that for a distant wind measurement of localwind pattern 751, the perceived source for plume 750 detected by sensorsystem 741 would be in the East sector, thereby leading to the incorrectguess that point source 749 is the source that is emitting plume 750.However, if the collocated measurement of wind direction at sensorsystem 741 is considered, plume 750 appears to be coming from areasource 743, which is also incorrect. Note that a simple, linear localback-tracing of the wind parcel from the perspective of the wind sensorin sensor system 741 would have led to the same bad conclusion that areasource 743 is the source since the terrain is the main source of thenon-linear wind flux geometry. What this example shows is thatidentification of sources from wind speed and direction measurementsalone is difficult without large numbers of wind measurements.

In one embodiment, fine measurements of wind around the site would betaken to properly measure the complex wind pattern responsible for theplume transport. Using multiple wind measurements can becost-prohibitive. In another embodiment, a simulation of the emissiontransport using a digital twin of the site is performed. Such a digitaltwin can reconstruct an estimation of the actual flux responsible forthe transport and consider the effect of terrain 747, obstacles 748,source geometry 743, 742, 749, as well as other parameters relevant forthe turbulent advection/diffusion of the target emitted compounds. Withthat simulation, the accuracy of the flux in the site is enhanced andcloser to the actual flux of air flow 746. Because of this, attributingthe plume 750 to point source 742 with a single deployed point sensor ispossible.

The same model may allow for reconstructing a detection limit 744 of thesensor system 741. Detection limit 744 denotes the limit for which thesmallest leak size is only detected 50% of the time. Other criteria fordetection limit 744 may be specified for different leak size ordifferent confidences of detection. In a perfectly flat model with auniform chance of wind in any direction, the detection limit at aconstant altitude is circular (approximated by a cardioid in threedimensions). In practical cases, the shape of the detection limit may bevery complex and may change based on wind pattern, temperature andpressure, terrain and other parameters impacting the transport of thecompounds as well as detection limits of the sensor itself. FIG. 7Cgives an approximation of the detection limit at constant altitude as anellipse. In this case, sensor system 741 is adequately positioned todetect emissions from sources 743, 742, and 749 as these potentialsources are within a range of the detection limit 744 of the sensorsystem 741. Note that other positions may lead to higher sensitivity tosources 743, 742, and 749 but the position of sensor system 741 may bedependent on other factors, such as land usage authorization, betterline of sight for communications, or network optimization positioningfor a deployment with more than one sensor system.

Multiple sensor systems as described in FIGS. 7A, 7C, and 7D may bedeployed in a field for the acquisition of weather measurement andcompound measurements. The sensor system takes these measurements andrelays messages related to these measurements with timestamps,identifiers, and other metadata regarding sensor operations to acentralized computing unit 727 in FIG. 7B. The communication of data andcommands is represented in FIG. 7B. Sensing unit 733, which may or maynot be the same as that described in FIG. 7A, can incorporate componentssuch as a power system 723, weather sensors 722, compound sensors 721, acomputing unit 724, and a communication unit 725. Sensing unit 733 canrelay messages, as described above, to centralized computing unit 727using network layer. The network layer may rely on existingcommunication infrastructure such as cellular or satellite, or dedicatedinfrastructure such as custom wired or wireless systems, including butnot limited to, Wi-Fi, Bluetooth, SCADA systems, LoRa, and othertelemetry and data transmission systems. The data transmission may relyon other network infrastructure, such as the Internet or on dedicatednetworks such as intranet or LAN. Sensing unit 733 may also directlytransmit messages to non-networked systems or local systems 734 as maybe the case for a local interface used by the sensor system user. Themessage from sensing unit 733 may be relayed through other sensor unitsas in daisy-chained or starred sensor system networks or through adedicated unit for the local storage, scheduling and packaging ofmessages from various sensing systems 733, 735, deployed in the vicinityof each other. This may be done to amortize the cost of expensivetransmission technology such as satellite links.

Once in centralized computing unit 727, message processing is performedto transform raw data into actionable data. This may include simpleoperations such as data formatting or more complex operations such ascreating a maintenance tracking system for the operator. In oneembodiment, the data processing is the conversion of weather andcompound measurements into detection, localization, quantification, andqualification of target compound emissions. To transform the rawcompound measurement into speciation and concentrations, an externaldatabase 736 such as the HiTRAN database may be queried for referencespectra, or internal databases of calibration measurements taken withthe specific sensing unit 733 during calibration runs. Other informationsuch as sensor units' metadata 738 may be used for the specificinstrument characteristics to enhance speciation and concentrationmeasurements.

In order to perform localization, quantification and qualification,centralized computing unit 727 may reference field metadata 737collected by field operators such as, but not limited to, topologicalmaps of the field deployment, images of site, the potential sources andequipment, equipment inventory and GPS coordinates of features ofinterest, for the purpose of creating a digital twin of the site for thepurpose of atmospheric transport modeling and simulation. Other fieldmetadata may include previous local weather information and the externalweather databases 736 are queried.

Centralized computing unit 727 may use other messages from anothersensing unit 735 for enhanced localization, quantification, andqualification of the emissions. Sensing unit 735 may include multiplesensing units and may be of the same type as sensing unit 733 or anyother sensing units present on the sites. For example, sensing unit 735may be a flare lighting sensor used as an indicator to help attribute anemission detected by sensing unit 733 to a flare misfiring.

Actuator commands may be used as a sensor feed as well. For example, theactuation of pneumatic equipment at oil sites may result in apredictable emission; therefore, command signals from actuators may beused to help predict expected emissions from an oil site. An example inthe landfill industry may be variation in the pressure head of wellswhich may be correlated with a local emission hotspot. This concept canbe extended to all existing command signals and process sensors alreadypresent in equipment associated with potential emissions sources.

Once detection, quantification, qualification, and localization ofsources is obtained by the processes in the centralized computing unit727, actionable data may be generated. Actionable data may mean the datanecessary to take a corrective action, including, but not limited to,emission reports, maintenance lists, maintenance tracking andemissions-reduction tracking tools. The actionable data may further beused as commands or scripts for automation systems 731. For example,actuators on a site may be automatically put in a safe position if anexplosive concentration of a flammable compound is detected. Anotherexample would be the operation of alert equipment such as sirens orvisual cues triggered to alert operators to perform emergency evacuationif a toxic compound is detected. At times, robotic or automatedinspection and repair or maintenance of equipment may be deployed as aresponse to a command. For example, a drone may be deployed to performprecise automated inspection of a certain area identified by sensingunit 733 to perform fine-scale equipment leakage detection. Anotherexample would be automated excavation equipment which can be deployedfor placing additional ground cover on a detected emission hotspot at alandfill. A third example would be triggering an automatedself-diagnostic system in a continuous production environment which mayrequire large computation power for distinguishing problems in theprocess.

Actionable data may be used to generate automated reports in documentgeneration task 732. For example, the sensor data may be used togenerate regulation-mandated emission inventory reporting and editauto-completed reports to be physically or digitally sent to theconcerned agency with or without operator intervention.

Actionable data, emission data and raw data may be transmitted to otherservers 730, that may be internal or external. The purpose of this maybe to relate raw data for archiving or post-processing, or to send datato servers behind a firewall in specific user instances whereproprietary data is collected and require different levels ofencryption. In that case raw encrypted data may not be decrypted in thecentralized computing unit 727 for data safety reasons and may only besafely decrypted behind a client's firewall.

Actionable data such as triage information, reports, maintenance, andabatement data may be communicated through emails, text messages,dashboards, or dynamic notebooks, to static I/Os 729 and mobile I/Os728. Static I/Os 729 can include PC and other fixed computing units suchas in the office of the field manager. Mobile I/Os 728 s can includepagers, PDAs, phones, tablets or laptop computing units and equivalentssuch as the phone of a field operator such as a pumper or a fieldforeman for oil and gas applications.

As seen in FIG. 7B, the centralized computing unit 727 processes themessages received by the sensing unit 733. Now referring to FIG. 8B, anembodiment of a method 800B executed in the central computing unit forconverting the information received in messages 840 originating from thesensing system described in FIG. 7A is described. The message generationprocess described in FIG. 8B converts messages 840 into actionable datain the form of emission detection, localization, quantification, andqualification as well as other actionable data generated by anactionability engine 837.

First, in step 830, message 840, which may be stored in a serverdatabase 870 after reception, is routed to the server instance that isresponsible for message processing. Message 840 includes the informationformatted by the sensor system of FIG. 7A and may be constituted ofspectral or concentration information as measured for a certain sample t(which is taken at the time t) as well as weather information and sensormetadata (such as, but not limited to, diagnostic parameters, GPSlocation and sensor ID). The message is first decrypted and decoded instep 831.

Step 831 of FIG. 8B first uses a decryption protocol associated with theencryption method employed in step 815 of the message formatting asdescribed in FIG. 8A. Message 840 may be parsed into three datasets: (1)raw spectrum 850, which may contain an absorption spectrum measured atsample t; (2) sensor metadata 851, which may contain sensor diagnostic,ID, GPS location and such; and (3) weather data 852 that may be takenaround the time of the sample t. Raw spectrum data 850, sensor metadata851, and weather data 852 may be stored in database 870 for futurereference or for recalculation if new computation methods are lateravailable. Note that raw spectrum data 850 is specific to an embodimentof the sensing technology and could be any other type of raw dataassociated with measuring the concentration of a target compound.

In step 832, the data of step 850 associated with the sensing of thetarget compounds is preprocessed. In an embodiment of step 832 for thespecific case of spectroscopy sensor technologies, a raw spectrum isprocessed. The preprocessing includes denoising the data, peak alignmentand bias shifting and computing an absorbance spectrum of sample t 853from the transmission spectrum by using a spectral baseline 841 as areference transmission. This step may involve sensor metadata forsensor-specific preprocessing, for example for accounting for lightsource power shifts, using sensor-specific information stored indatabase 870. Generally, regardless of the sensing technology embodimentused, step 832 may involve denoising, debiasing, or otherwisecalibrating and enhancing the raw signal with preprocessing strategiesthat may involve sensor-specific information such that the preprocessedsensor signal may be analyzed.

In step 833, the preprocessed sensor signal may be analyzed forspeciation. The process of speciation involves the identification ofvarious compounds from a raw signal. For example, FIGS. 8C-8D illustratea front view 800C and a top view 800D, respectively of an example site.The site may include multiple potential emission sources E1, E2, etc.Further, the site may include a sensor S1. In the scenario depicted inthe FIGS. 8C-8D, a target compound C1 is emitted from the source E1 andforms a plume P1 covering a region R1. Further, an obstruction O ispresent which may obstruct the plume P1. As such, the obstruction mayresult in a region R2 within the region R1 where the target compound C1is not present or is minimally present. The sensor 51 which may be lyingwithin the region R1 but outside the region R2 may detect the targetcompound C1.

Referring now to FIG. 8E, a top view 800E of another scenario withrespect to an example site is shown where mixing of multiple targetcompounds takes place. As shown in the FIG. 8E, the site may includemultiple potential emission sources E1, E2, etc. Further, the site mayinclude the sensor S1 and S2. Target compound C1 is emitted from thesource E1 and forms the plume P1. Further, a target compound C2 isemitted from the source E2 and forms a plume P2. The plumes P1 and P2merge in a region R3. As such, the region R3 includes both the targetcompound C1 and the target compound C2. The sensor S1 which may be lyingoutside the region R3 may detect only the target compound C2. The sensorS2 lying in the region R3 detects both the target compounds C1 and C2and therefore generates a confounding signal.

Referring back to FIG. 8B, the process of speciation therefore involvesmay involve identifying the contribution of one or multiple targetcompounds and separating them from confounding signals. This step maynot be necessary for single-compound sensor signals that have noconfounding elements. For the specific embodiment of a spectrometerabsorbance signal, the identification may involve using reference targetcompounds spectral profiles 842 and minimization, inverse, or inferencemethods to decompose the spectrum into its component spectral signaturesassociated with target compounds. The target compounds profiles 842 mayoriginate from the database 870 or from external spectral databases 871,for instance a HiTRAN database. A residual spectrum may be left overthat may contain noise, non-linear contributions from light source andsensor bias over time and a spectrum of non-target compounds that may ormay not be known.

A sample composition 854 for the sample t taken at time t is generatedand stored in the database for the target compounds. This may furthercontain a residual signal for further analysis. Once the composition isidentified, the concentration of each target compound in thiscomposition may be obtained in step 834. For certain embodiments of thesensor technology, the concentrations may be obtained directly fromsteps 832 and/or 833. In one embodiment related to spectroscopy, thecomposition may be identified for normalized target compound profiles instep 833; the purpose of step 834 is then to associate the normalizedprofiles to a certain concentration. This may be achieved by using datafrom a specific sensor calibration data 843 as stored in the database870. This calibration data 843 may be obtained by testing the specificsensor that has taken the sample t, or a reference sensor of the sametype, with a multiple point test against a calibrated compound mixture.For example, when measuring methane, a five-point calibration in therange of concentration of interest such as 0 ppm to 100 ppm may betaken. The calibration data 843 associates a known concentration tospectral profile intensity, which may be nonlinear and can use thecomposition of speciated spectra from steps 833 to derive theconcentration of each species of the composition. The sample tconcentration data for the target compounds 855 is stored in thedatabase 870.

Once the sample composition and concentration with respect to targetcompounds are found for a certain sample t, the localization,qualification, and some of the quantification is performed in steps 835and 836. Step 835 focuses on solving an inverse transport problem. Thatmay include a representative fluid mechanics model and models of thegeometry, topology and other characteristics of the site surrounding thesensor system responsible for a set of measurement samples t. The modelis created to recreate the condition in which the compounds of interestmay be transported from the prospective sources and other confoundingsources to the sensor system. The direct problem creates the relationsbetween causes, such as source location and emissions flux for eachsource, weather at the sources, and consequences, such as sensorconcentrations measurements and weather at the sensor.

The inverse model identifies the inverse relation; that is, finding thesources of emissions and emission intensities knowing the sensorsystem's measurements. This may be done explicitly by running a set ofreference transport simulations 844 that may be stored in the database870 for reuse and creating an inverse relation matrix to relatemeasurements with sources. Two inverse problems may also be run by firstsolving the flux inverse problem, i.e., finding the weather conditionsat boundaries of a simulated domain that would result in the observedweather measurements by the sensor system, and then solving for thetransport inverse system, i.e., what source's emissions would haveresulted in the observed compound concentrations in the weatherconditions found in the first inverse problem. The inverse problem mayalso be solved by modifying the transport equations such that they mayrun backward in time. Furthermore, local weather during the period ofinterest T 846 from external databases 871 may be used to enhance theselection of appropriate initial and boundary conditions in the solvingof the direct or inverse problems. Together with this inverse problemsolving, uncertainty quantification may be used to enhance the resultand/or reduce the burden of large simulation sets by rewriting theproblem as a function of probability distribution functions of the inputparameters, formulating prior probabilities, and using statisticalinferences such as Bayesian methods. This can help in sourceidentification by explicitly solving for the probability that aprospective source is an actual source given the sensor systemmeasurements and may reduce the number of direct simulations to anacceptable minimum based on known error distributions. Results of thetransport simulations for a certain period T 857 may be stored.

In order to improve the inverse transport problem in step 835, all thesamples tin a data period T 856 may be used by a solver algorithm. Thisis important because each sample t constitutes only a snapshot of thesite for a given weather pattern. Therefore, in order to both buildaccuracy through repeated observations and in order to increase coverageof the site, a certain number of samples t are used, all within acontiguous period T. The period T is selected based upon the expecteddetection speed, the time necessary for the weather pattern, inparticular the wind direction and speed, to change sufficiently suchthat the detection of potential emission for the observed sources ispossible and based on the expected accuracy of reporting. The period Tcan shift from 1 minute, for example for the detection of criticallydangerous compounds where emergency protocol may be engaged, to multiplemonths, for example at remote sites where intervention may not bepossible or of concern for long periods of time. The period T may bedynamically, manually, or automatically allocated based on the sample tconcentration 855 and composition 854, operator requirements,maintenance schedule, hazard, duration of emission and such.

Without loss of generality, the longer the period T, the higher theaccuracy of the inverse transport problem in identifying averageemissions over the duration T and with higher spatial resolution.However, in case of the identification of transient emissions, that is,emissions that may be intermittent rather than continuous, it may bebetter to select a duration T that matches the expected timecharacteristics of such emissions. In the embodiments related toupstream oil and gas, a judicious period T may be 1 to 2 weeks foremission monitoring purposes and shorter for safety purposes. In theembodiments related to solid waste, such as solid waste landfills andcomposting operations, a judicious period T may be from 5 days to amonth when identifying cover hotspots and shorter for diagnosing wellfailures and for safety purposes.

The inverse transport problem 835 may be solved for various periods Tusing the same dataset in order to achieve different objectives. Oncethe inverse problem is solved, both the emissions probable sources andemissions flow rates for the selected period T, 858, are identified andstored in the database 870. This allows both the quantification (byidentifying emission fluxes) and localization (by associating theprobable sources with the site's equipment or areas). Then, emissionsare to be qualified in step 836. The qualification allows furtherrefinements of the understanding of the emissions. Indeed, emissions cancome from different elements within an equipment but more importantly,emissions from the same equipment may be separated into categories ofexpected emissions from normal operation and spurious emissions fromleaks or abnormal operations. For example, in the upstream and midstreamoil and gas industry, equipment such as compressors and pneumaticactuators may emit methane in normal operation. In another example,landfills may have diffuse emissions depending on the presence or typeof cover. This means that successfully detecting, localizing, andquantifying an emission may not mean that a leak has been detected, andmay in fact indicate that the site is operating as designed.

One embodiment of step 836 uses statistical inference together withemission statistics 847 to identify the type of normal emission or leaksby distinguishing their intensity, frequency, and composition over timeduring a period of interest. Each emission type indeed has a specificsignature in terms of intensity, frequency, and composition over time.Matching these signatures with the observations allows for theidentification of emission profiles or outliers. The emission statistics847 can be generated as a composite from equipment characteristics, forinstance by accessing external databases 871 such as the EnvironmentalProtection Agency (EPA) expected average emission by equipment type, byin situ statistical quantification by observing the emission profile ofa site under normal operation, or by any other suitable experimental ortheoretical methods to create such emission statistics. Statisticalinference may be used to classify the emission type by source 859 whichmay be saved in the database.

The accuracy of the statistical inference can be improved by integratinga feedback loop, such as using operator data and/or maintenance logs848. Indeed, these logs may be used to positively identify that acertain footprint was really indicative of a certain emission type. Analternative method to this qualification method embodiment may be to useartificial intelligence, machine learning, or neural networks. In thiscase, a training set is first created to identify the signatures of theemissions. The artificial intelligence method may learn over time byaccumulating validation information from the type of emission throughthe site operator maintenance log 848. Over time, emission types may bemore and more accurately qualified by a learning algorithm.

Once emissions are detected, qualified, quantified, and localized, thisdata needs to be provided in an actionable form to the end user. Anactionability engine 837 provides this additional layer of intelligenceby matching the characteristics of the emission to the needs andobjectives of the end user. The actionability engine 837 may interpretthe emission data, together with the maintenance logs 848, to providethree categories of actionable information. Some additional categoriesmay be extracted from the data as well, may the need arise.

The first category is messages and alerts 860. The purpose of these isto relate the relevant information to the operator, to provide either acall to action or a status update. The messages may be, but aren'tlimited to, an indication of the current state of emissions in all thecovered sites, a triaged list of emission flags ranked by intensity orgravity for maintenance intervention, and alerts in case of critical oremergency conditions due to the emitted compounds. For example, in theupstream oil and gas industry, a ranking of notifications or virtual“flags” for potential fugitive emissions may be sent to a field foremanin order to prioritize sites for inspection and maintenance. In anotherexample, an alert for high concentrations of hydrogen sulfide may besent to all field operators or pumpers in the vicinity of a dangeroushydrogen sulfide leak at a specific well pad. In yet another example,the emission inventory for a specific site for a quarter may besummarized for an operational field manager to track emission inventoryobjectives. This first category of actionable insights provides one-way,summary information that may be used for metrics tracking, safetyalerts, or maintenance scheduling. Fundamentally, this first categorydoes not have a feedback loop.

The second category generated by the actionability engine 837 may be amaintenance tracking system 861, where information from the operator maybe used, for example as maintenance log 848, to actively update themaintenance strategy. In the oil and gas industry, the maintenancetracking system 861 could be used to track and schedule maintenanceefforts based on available resources and to flag resolutions. Forexample, the maintenance tracking system 861 could limit the number offlags by avoiding notifying the operator multiple times for the sameemissions until the emission is marked as fixed. For example, in thelandfill industry, the flag associated with a particular hotspot may besuspended until the site manager confirms that cover remediation wasattempted by adding more cover around the hotspot. In effect, themaintenance tracking system 861 can help ascertain that remediation fora particular emission was successful. The maintenance tracking system861 may also suggest the most likely faulty component to look for basedon a site's known equipment inventory. The maintenance tracking system861 may directly allocate works to various maintenance teams based onavailability of tools, human resources, and time. The maintenancetracking system 861 may suggest replacement parts when repeated leaksare detected from certain components, for example a particular actuatorbeing known as faulty may be replaced by a better model to avoid therepeat maintenance cost. The maintenance tracking system 861 may send atriage list of unwanted emissions by intensity and suggest interventionspeeds for each considering the likely maintenance cost, lost gas, andresource intensity requirements. For example, in upstream oil and gas,an open thief hatch identified as the likely emission source from aliquid tank would be rated as a high priority, as it is a high emitter,does not require specialized equipment to address or find, does notrequire large amounts of human resources to address, and is easy toverify. The maintenance tracking system 861 therefore does balancepractical requirements with emission reporting for maintenance purposesand make use of operator feedback in its updating.

A third category of actionable information lies in abatement or emissionreduction tracking 862. For this category, information over a longertime trend is analyzed. By collecting maintenance information andemission over long periods of time, emission inventories and repeatequipment failures may be compared across many sites. This may allowranking sites which are attempting emission reductions by variousstrategies. For example, one site may use compressed air actuators inone oil field and low-bleed actuators in another and compare emissionintensities of both technologies in real life conditions. This may leadto better decision making when implementing pilots for new, loweremission technologies. In particular, the cost per avoided ton of CO2equivalent may be compared when using an embodiment technology whichtracks greenhouse compounds. Emission inventory trends may be used toevaluate the efficacy of practice or equipment change using theabatement tracking system 862.

In general, the actionability engine 837 may involve a set of rules,algorithms, and artificial intelligence to generate actionable data forthe messages and alerts 860, maintenance tracking 861, and abatementtracking 862. This actionable data may be stored in the database 870 andmay be communicated to stakeholders in step 838.

FIGS. 8A and 8B have detailed an embodiment of a process for theconversion of weather and compound sensor measurements for emissiondetection, qualification, quantification, and localization, and for thegeneration of actionable data, insights, and maintenance and emissionreduction tracking. The described method is not limited with respect tothe type of sensor technology. The following presents theparticularities associated with signal treatment in the near tomid-infrared region for an embodiment of the sensing technology whichutilizes absorption spectroscopy. This region of the spectrum is ofparticular interest for detecting greenhouse gases, which are gases thatabsorb electromagnetic radiation in the infrared part of the spectrumand contribute to the trapping of heat when present in the atmosphere.

With regard to FIG. 9 , a schematic representation 900 of absorptionspectroscopy as a concept is given. The basic concept of absorptionspectroscopy is to identify the presence and concentration of a compoundby its ability to absorb light of particular wavelengths. An absorbanceof a sample depends on concentration of the various compounds presentand on a pathlength of light through the sample. This relation may begiven by the Beer-Lambert law, which relates the absorbance of a certaincompound to its concentration in a mixture. Compounds may be identifiedand distinguished by the unique set of wavelengths that they absorb.

The particular embodiment presented in FIG. 9 may use a source ofinfrared light 910, a gas cell 911, and a microspectrophotometer orinfrared sensor 912. The light source 910 produces infrared light in thespectral region of interest. This source 910 may be a laser or lightpanel (e.g., LED light panel) with an acceptable spectral width, oranother emitting source such as a filament or a thin-film resistivesource which may behave similarly to blackbodies emitters. The sensor912 may be a spectrophotometer or a light sensitive detector. Thissensor 912 may be sensitive to light in the spectral region of interest.The spectrum may be obtained from tuning a laser to scan the spectrum ofinterest such as in a Tunable laser Diode Absorption Spectroscopy orTLDAS method, by using a scanning microspectrometer (such as in FourierTransform Infrared or FTIR or Fabry-Perot Interferometry or FPI-basedsensors) where a specific wavelength or interference pattern issub-selected for observation in turn, or by having diffraction-basedmicrospectrometer where the light is sorted based on its wavelength andprojected onto a sensitive sensor array. One embodiment relies uponFPI-based microspectrophotometry using a broadband resistive lightsource 910. However, this configuration of the system, i.e., for theprocessing and analysis of spectra, may be applied to any sensingtechnology resulting in a measured spectrum of light and is in no wayrestricted. In particular, absorption spectroscopy relies ontransmitting light through the sample, but aspects of the signaltreatments can apply to different spectral methods, for example forRaman or reflection. In the case of the observation of particular mattercompounds, nephelometry may be an alternative to spectroscopy, where theobserved light is not the transmitted light through the sample but thescattered light at an angle from the cell that is not the transmittedlight angle.

The sample cell 911 may be a cavity which contains a sample. The samplemay be input through an entry point 913 and output through an exit point914. Entry point 913 and exit point 914 may be the same point and thesample may be naturally fed into the sample cell 911 by wind (in thecase of an open path spectrophotometer) or aspirated with a pumpingsystem. Light from the source 910 interacts with the sample in thesample cell 911 and exits the cell to be collected on the sensor 912.The interaction occurs over the pathlength of light within the cell andthe absorbance of the sample increases with pathlength. Thus, detectionthreshold and precision of the instrument may be improved by increasingthe pathlength. Optical elements 917 and 918 may be used to collimatethe light from the source 910 and collect or focus light on the sensor912. Optical elements 917 and 918 may be reflective or transmissiveoptics elements or element groups suitable for collimation and focusingthat would result in low diffraction and chromatism. These elements 917,918 may or may not be imaging. In one embodiment, a single off-axisparabola is used for both optical elements 917 and 918.

The sample cell 911 may also contain optical elements to increase thelight pathlength within the sample. Common strategies may rely onmultipass cells such as Herriott, White, or circular cells. Otherstrategies may increase the pathlength by measuring extinction rate suchas in cavity ringdown spectroscopy where pathlength can be increased tokilometers by creating a cavity with highly reflective surfaces. Oneembodiment uses a Herriott cell with collimated broadband light from athin-film source, which is a novel way of using a Herriott cell with abroad source.

Graph 915 gives an example of a transmission spectrum before interactionwith the sample. Graph 916 shows the transmission spectrum afterinteraction with the sample. In 915, the transmission spectra emitted bythe source 910 is presented. In this example, these spectra are akin toa transmission spectrum of a blackbody radiator with a peak at 3.5 um.In the case of a typical tunable laser, this may look like a very narrowband of the spectrum (typically less than 10 nm), but some broadbandtunable lasers exist as well. The light then proceeds through the samplecell 911 and exits after interaction with the sample. The resultingspectrum is depicted in 916. The black line spectrum is the exitspectrum, to be compared with the light gray curve representing theentry spectrum. An absorption peak 919 is identified. This is indicativeof the absorption of a part of the light by the sample. The peak shape,width and position in the spectrum is indicative of the compoundsignature, and may depend on the instrument transfer function,temperature, pressure, and concentration of the compound in the sample.Other compounds in the sample may also broaden peaks. In the case of alaser-based system, the spectrum of the laser light may be very narrow,resulting in a very narrow transfer function and to the observation ofthe fine structure of the spectrum signature of the compound. Somespectrophotometers have a very large detection half-width resulting inthe impossibility of observing the fine structure of a compound'sspectral signature. It may therefore be difficult to identify andspecify spectra obtained by large half-width spectrophotometers as thespectral signature of many compounds may look similar or maysuperimpose. While the following signal processing may apply for allspectral methods presented herein, it is specifically adapted for theinterpretation of spectrum with transfer function resulting in thesuperimposition of the compound signatures.

Now referring to FIG. 10 , a flowchart of a method 1000 of processing ofabsorption spectra is illustrated. A graph 1041 presents a raw spectrum1051 obtained from a compound sensor such as those previously described.This is denoted by raw transmission spectrum data 1020 stored in adatabase 1040. The raw transmission spectrum data 1020 (noted 1051 ingraph 1041) is first preprocessed in step 1010. Step 1010 performs basicoperations to improve the accuracy of the spectrum such as denoising,debiasing, and peak alignment. One potential embodiment of denoising mayuse a wavelet method where the transfer function of the potentialinstrument at various wavelengths is used to generate a wavelet basis.Such a method would allow for the rejection of noise which is notprojectable on the wavelet basis, or which only contributes to thehigher order wavelets. This may allow a significant reduction of noisefrom the raw signal. Debiasing may be performed on the raw signal or onan absorption spectrum by comparing the baseline absorbance to theexpected baseline. This process is simplified by an embodiment of thedebiasing which uses the wavelet described in this disclosure. Whenprojecting to a wavelet basis, the lowest-order wavelets are indicativeof the baseline and may be deleted with minimal loss of informationabout the absorption resulting from the target compounds. Peak alignmentmay be performed using reference absorption peak or baseline profilematching. For example, one potential embodiment of this is to use theCO2 peak at 4.2-4.3 um that is present within the observed spectra 1051for the alignment, since CO2 has a high concentration in ambient air.Baseline matching may be obtained by the wavelet method by observing thebaseline in the lower order-wavelets, resulting from the source emissionprofile, and by matching the profile to the known emission spectrum ofthe source.

The denoising, debiasing and peak alignment may result in the correctionof the raw transmission spectrum 1051 into a processed transmissionspectrum 1052. A light emission spectrum 1050 is given, which would be anative emission spectrum of the light source before interaction with thesample.

When preprocessing is finished in step 1010, an absorption spectrum isgenerated in step 1011. Absorbance is calculated from the transmittanceobtained from the sample compared to a reference transmittance, whichdid not go through the sample. One example is to generate a differentialabsorption spectrum by using a sample observed at the site as areference sample. This reference sample is selected from its lowabsorption in the zone where target compounds are searched, for example,from a wind direction where no sources are in the vicinity. The purposeof this is to eliminate the particular background of the site and tokeep an updated reference sample to compensate for biases in the systemfrom loss of performance of the light source, the sensor, or the optics.A reference sample is selected and taken as a reference baselinespectrum 1023 from the database 1040. This reference baseline spectrum1023 is comparable to the light emission spectrum 1050 of graph 1041.The absorption spectrum 1011 is generated using the equationA=−log(T/T0), where A is a resulting absorption spectrum 1030, T is theprocessed transmission spectrum 1052 at step 1010 and T0 is thereference baseline absorption spectrum 1023. As an example, theresulting absorption spectrum 1030 is plotted as curve 1053 in graph1042. The resulting absorption spectrum 1030 may be stored in thedatabase 1040.

A second part of the processing may involve identifying compounds andtheir concentrations by their absorption signatures, and avoidingconfounding agents such as unknown factors, residual biases, and noisein the signal. One embodiment of this identification may involvedecomposing the absorption spectrum 1030 into its component absorptionspectra, each associated with a target compound as well as a residual.To do so, one may use a wavelet method. In one embodiment, asensor-specific wavelet basis 1032 to decompose the absorption spectrum1030 is used.

A sensor transfer function 1022 is recovered from the database 1040.This may be measured for each sensor system or calculated from measuredtransfer functions of each subsystem. A wavelet basis may be constructedfrom the set of transfer functions 1022 when fit with Gaussian functionsor other suitable fit. A wavelet generation method may be used forconstructing wavelets in step 1013 resulting in a sensor-specificwavelet basis. In parallel, spectra of the target compound 1021 may berecovered from the database 1040. These target spectra 1021 may beconstructed from reference measurements or obtained from an externalsource such as the HiTRAN database. In step 1012, the sensor transferfunction 1022 is used to convert the reference target spectra 1021 intosensor-specific target spectra 1031 in a process akin to a convolution.This transformation simulates the peak broadening that would result fromobserving the target compounds' spectra through the sensor system. Step1012 may be omitted if the sensor-specific target spectra 1031 aregenerated from experimental measurements on the sensor for each targetcompound.

In step 1014, a search space is formed for speciation of the absorbancespectrum 1030. To do so, a new basis is generated from thesensor-specific target spectra 1031 and with quasi-orthogonal residualbasis selected from the sensor specific wavelets 1032 such that theresulting basis is complete over the absorption spectrum 1030. Thisbasis forms a sensor and target-specific search space 1033, where anidentification method 1015 is applied. Other basis generation methodsmay be used, to generate a quasi-orthogonal residual basis to form thesearch space.

In step 1015, a regularized minimization method under constraints may beused on the absorption spectrum 1030 to identify the coefficientsassociated with each term of the functional basis forming the sensor-and target-specific search space 1033. An example minimization method isa least-squares method under positivity constraints. Other minimizationnorms may be used besides the L2 norm, as well as other identificationstrategies based upon wavelet transform, Monte Carlo search, geneticalgorithms, bayesian inference, neural networks or other machinelearning strategies which may require experimental training sets ofvarious spectra with known compositions. In some of these identificationmethods a search space 1033 may not need to be generated as it may beimplicitly part of a prior training set.

Resulting from step 1015 may be a detected composition of the samplewith coefficients associated with each target spectra 1034 and aresidual absorbance 1035, which may contain information related tonoise, biases, and unknown compounds. A graphical representation of thisis given in graph 1042, where target spectra 1055 and 1056 may beidentified from the absorbance 1053, as well as a residual 1054.

One particularity of the embodiment is that spectra originating fromunknown compounds may be identified with the aforementioned method evenif their spectra significantly overlap with the spectra of targetcompounds, specifically because the proposed search space relaxesconstraints related to finding a fit with an unknown absorption profile.

In step 1016, the sample ratios and composition 1034 related to targetcompounds may be analyzed to identify the concentrations of thespeciated mixture. For this, calibration results 1023 which relate thecoefficients associated with the reference target compound spectra 1021with the compound's concentration are used from the database 1040. Thesecalibration results are obtained from experimental measurement using thesensor system or a reference sensor system and may also be calculatedfrom a known absorption cross-section of the target compound stored inan external database such as HiTRAN together with a known pathlength ofthe instrument. This results in an estimation of the concentration ofthe target compound present in the sample 1036, which may be stored inthe database 1040. Parallel to this, a residual absorbance 1035 may beanalyzed in step 1017. This may involve reviewing prior residuals fromprevious samples on the same instrument for comparison. By doing so,unknown compounds that are repeatedly observed 1037 may be isolated overtime, and noise, bias, and other errors may be qualified and quantified1038. This can provide valuable insight on the accuracy of the sampleidentification in the form of error indicators, that may be used togenerate upper and lower bounds in the identification of the targetconcentrations 1036. The unknown compounds 1037 and noise, bias, anderror 1038 data may be stored in the database 1040.

Note that the analytics method presented in FIG. 10 may not only beapplied to airborne or gaseous spectra but in general to any spectrum,and in particular those taken by low spectral resolution instrumentswhere signal overlaps may be common. This may be applicable toidentification of compounds in liquid spectroscopy, for instance in milkquality analysis, alimentary oils, lubricants, fuel and so on. In thesecases, the optical cell may be reduced to a thin layer such that thecomposition of the sample may be observed without extinction at certainwavelengths. For example, a mid-infrared (2.5 to 25 um) methodology maybe applied to identify water, soot, oxidation, nitration, sulfation, orother composite indices such as total base and acid numbers, and may beapplied as a real-time, in-line instrument for the evaluation oflubricant quality on mobile or stationary mechanisms requiringlubrication.

FIG. 7C presents how a sensor system may be deployed in the field in amanner accounting for terrain, potential source location, transportobstacles and wind pattern. The underlying principle for uncovering asource is to sample from the plume of said source when the winddirection and speed point (in an average sense) form a line from theemission source to the sensor system.

The fundamental principles of this plume detection are detailed in FIGS.11A-11E. FIG. 11A presents a symbolic top view 1100A of the transport ofan emission plume 1123 from a source 1121 to a sensor system 1120 viatransport denoted by streamline 1122. In reality, the plume 1123 may notbe contiguous and may have a complex three dimensional shape. FIG. 11Apresents the transport in the case of a steady medium-speed windpointing directly to the sensor system 1120. FIG. 11B illustrates asimilar symbolic top view 1100B but with a faster wind speed. FIG. 11Cillustrates another symbolic top view 1100C showing effect of a changein wind direction. FIG. 11D yet another symbolic top view 1100D showingthe effect of a tortuous streamline. FIG. 11E illustrates a symbolicrepresentation 1100E of construction of a plume cross-section using thewind direction to ‘scan’ across the plume.

Comparing FIGS. 11A to 11B, it can be observed that an increase in speedmay result in a narrower plume extent since the plume spread isdetermined by a balance between diffusion and turbulent mixing, andadvection, and at higher wind speeds, horizontal advection becomes thedominant force. This results in a change in an observed concentration atthe sensor system 1120, namely that a maximum concentration observedacross the plume may be higher in the case of higher wind speeds.However, higher wind speed can also result in more turbulent mixing insome conditions which may influence this result, particularly resultingin a large spread of measurements of maximum concentrations. This changefrom low speed to high speed clearly denotes the importance of windspeed in transport, and therefore the necessity to measure wind speedwhen measuring concentrations of the emitted compounds.

In FIG. 11C, the average wind transport is shifted angularly compared tothe direct line from the source to the sensor as in 11A and 11B. Angle1127 is denoted “a”. In idealized conditions, an increase in “a” mayresult in a reduction of the observed plume concentration. Theconcentration in an idealized plume is maximum at the center. Inpractice, due to turbulence, the plume may be branched, and its crosssection profile may not follow a regular pattern like the one shown inFIG. 11E. FIG. 11E presents an idealized profile of the cross section ofthe plume as measured by the sensor system 1120. The sensor system 1120may sample the plume at different angles and register an associatedconcentration point 1124. When sufficient numbers are obtained, a fit ofa point cloud 1125 can be obtained. If the measurements occur inidealized conditions when the wind speed, temperature and otherparameters beside wind direction are stable, the plume flux may becalculated using a simple mass conservation equation by multiplying thearea concentration of the plume cross section by its normal speed and byestimating the plume concentration in the height direction. Thisapproach may be taken using plume theory for the estimation of the plumegeometry and using a mobile sensor across the plume cross section toestimate the average plume concentration.

One illustrative configuration instead uses shifts in wind direction toestimate the plume average concentration, as depicted in FIG. 11E.Another, more precise embodiment is given in the description of theinverse model used to estimate emission source and flux. The wind maychange dynamically during transport from the source to the sensor system1120, as shown in FIG. 11D. FIG. 11D shows a case where the transportfrom source to sensor is on average direct as denoted by an average flowdirection 1128 but may have a dynamically tortuous path. Moreover, awind direction as sensed by the sensor system 1120 is shown as vector1126. This exemplifies that in case of dynamic wind or when the topologyinfluences the actual path taken by air flow, the source position maynot be given by the wind direction measurement at the sensor system orat the source. This exemplifies the need for modeling of the air flow inthe vicinity of the sensor to better understand the transport of theemission from a source to a sensor system when dynamic effects,obstructions, topology, or other effects may influence the transport.

An embodiment of processes 1200 and 1300 for transport modelingincluding an inverse solver to identify source location and emissionflux is given in FIG. 12 and FIGS. 13A-B, respectively. In both of thesemodels, a digital twin of the site where the sensor system is placed ismodeled and may include, without limitation, topology, obstacles,equipment on site, potential emission sources and a model of the sensorsystem itself. FIGS. 13A-B propose a complete solving of the problemwith two distinct inverse problems being solved, one to identify theflow over the site and a second to identify the sources and emissionflux. In FIG. 12 , a single inverse problem is solved for localizing thesources while the weather over the site is found by matching the weathermeasured at the sensor location of a pre-simulated set of weatherconditions. The method 1200 depicted in FIG. 12 may be used when theflux is easier to define such as when the wind is constant duringsampling, with the advantage that most of the direct simulations can becarried out in advance and reused for subsequent source and flowidentification. The method 1300 in FIGS. 13A-B may be suitable when thewind conditions are shifting during sampling and in complex transportcases. The method 1300 in FIGS. 13A-B may take longer to compute as itmay be necessary to run direct simulations for the data processing everytime a sample is analyzed. In both methods in FIG. 12 and FIGS. 13A-B, aprobabilistic approach such as prescribed in uncertainty quantificationor statistical inference methods may be used, in which case thesimulation variables may be described as probability density functionsin order to propagate an error estimator to the results such as aprobability of positive source identification and error estimation onthe predicted emission fluxes. The advantage of such a probabilisticmethod is that the number of pre-simulated models may be reduced ortailored to the precision requirement, thus limiting the computationalcost of the methods.

Further, a method of identifying a target emission at a site isdisclosed. The method may include creating at least one simulation modelfor the site based on simulation parameters. The simulation parametersmay include a wind direction, a wind speed, an air pressure, an airtemperature, a number of potential emission sources, a location of eachof the potential emission sources, a source flux associated with each ofthe potential emission sources, a surface concentration, a weathercondition, a hygrometry data, and an altitude. According to the method,actual parameters for the site corresponding to the simulationparameters may be received, receiving actual emissions measurements froma plurality of air quality monitors deployed at the site associated withthe actual parameters for the site may be received. The plurality of airquality monitors may be deployed at predefined locations at the site.The method may include identifying a relevant simulation model from theat least one simulation model. It may be noted that the simulationparameters associated with the relevant simulation model match with theactual parameters. The method may further include extracting virtualemissions measurements generated by the relevant simulation model, andreceiving actual emissions measurements from the plurality air qualitymonitors deployed at the site associated with the actual parameters forthe site, correlating the virtual emissions measurements with the actualemissions measurements from the plurality air quality monitors, anddetermining configuration of at least one emission source based on thecorrelation. The configuration of emission sources may include alocation of the emission source at the site and a concentration ofemissions from the emission source.

In particular, with respect to FIG. 12 , a digital twin (also, referredto as simulation model in this disclosure) is first constructed in step1211. To construct the digital twin, field meta data 1220 (part ofsimulation parameters) is collected. The field metadata 1220 includesall the relevant information about the site in the vicinity of thesensor system at least containing the detection range of the sensorsystem. The field metadata 1220 may use satellite images, altitude, andtopographic data to reconstruct the terrain, location of equipment, typeof ground cover and so on. Furthermore, field metadata 1220 may becollected by an operator to ascertain the relative position of theequipment and sources relative to the sensor system, GPS coordinates ofthe sensor, list of potential obstacles, actual covers such as grass,earth, and trees, as well as a series of pictures of the site. These canbe used to properly identify a geometry of the site and surfaceproperties that may influence the simulation. Additionally, a threedimension cloudpoint of the site may be obtained by one or more scans,such as a LIDAR or radar scan. A numerical mesh or simulation grid isthen constructed to represent a section of the atmosphere around thesite that includes ground topology, large surface covers like trees, aswell as discretization of equipment, obstacles, and the sensor system.Atmospheric borders of that mesh are defined as boundaries for thesimulation and properties such as friction and slip may be attributed tosurfaces associated with ground, cover, equipment, and other topologicalfeatures. Sources are located in the mesh and identified as potentialboundaries to specify emissions. The simulated volume may be as small as100 by 100 by 100 meters (m) and as big as necessary to include a largefield network, and the characteristic mesh size may be as small as 10 cmand as big as 100 m.

For example, consider the case of an upstream natural gas well pad. Thewell pad may be 100 m by 100 m. A sensor system is positioned on thissite. Assume for this example that the sensor system is configured todetect methane and is accurate enough to detect leaks within 200 m inthe conditions encountered at this pad. Satellite images give anaccurate view of the pad, containing wells, separator groups and liquidtanks. The pad surface is identified by the operator pictures as graveland the pad is surrounded by hilly grassland. The altitude is given as500 m with a continental climate. A patch of pine trees lies to thenorth. Topological survey maps of the site are obtained in a nationaldatabase. The sensor is positioned in the north corner of the pad andangular position and distance of the different equipment group ismeasured by an operator for validating the satellite images. From allthis information a three-dimensional numerical mesh may be generated byan engineer. The mesh is a box roughly 300 m long by 300 m wide by 200 mhigh for this example with a mesh size of 1 m. The terrain is firstcreated using point cloud extraction from the topological map. Theequipment position is marked and three dimensional models of theequipment group, generated with a 3d modeling software, are positioned.The patch of trees may be added as individual trees or as a forest blockwith an appropriate diffusion model attached. The different terrainrugosity is attributed to the appropriate mesh elements. Boundaries ofthe 300×300×200 m box are specified for boundary and initial conditions.The surface of the potential sources, here the equipment groupsincluding the well heads, separators, and tanks are identified assources boundaries.

Consider another example, in the case of a landfill. The landfill may be400 by 400 m and includes a 35 m high mound. Four sensor systems arepositioned on the landfill. The landfill is surrounded by forested areasand the surrounding terrain is more or less flat. A service buildinglies to the north along an access road and a flare with a collectionpond to the east. The landfill operator provides an up to datetopological survey of the landfill. In this case, the mesh is1200×1200×500 m with a mesh grid size of 5 m. The building and flare are3d rendered as obstacles and the dense forest is represented as adiffuse cover group. The surface of the landfill is denoted as a sourceand divided into sectors of interest based on the landfill cover typeand based on the location of the sensors. Each sector's emission may beevaluated individually in order to determine the presence of emissionhotspots. The flare is also noted as a potential point source as are theindividual wells over the landfill.

Parallel to the task of generating the geometry of the mesh and meshsurface classification in step 1211 (digital twin generation),simulation flow and transport parameters 1222 may be introduced. Theseparameters may facilitate the simulation by providing values forinternal parameters such as diffusion of the target compounds in air,buoyancy of the compounds and boundary parameters such as typicalatmospheric wind profile at the site's altitude and location, frictionalparameters associated with cover type and such. These parameters 1222may be collected from scientific studies, external databases, orexperimental data. These parameters 1222 may be added to a generateddigital twin 1230 to constrain and bound the digital twin 1230. Notethat FIGS. 13A-B have the same process for generation 1311 of a digitaltwin 1340, from field metadata 1330 and flow and transport parameters1333.

Now with respect to FIG. 12 , consider a generation of initial andboundary conditions in step 1210. As stated earlier, FIG. 12 denotes anembodiment of the method where a set of reference simulations isconducted a priori to identify the relation between the virtual sources,their emission flux and the concentration measured virtually at thelocation of the sensor system in the digital twin simulation undersimulated weather conditions. To do so, this set of referencesimulations may need to include a large dataset to encompass weatherconditions likely to be observed at the site as well as a combination ofemitting sources at likely emission rates. A set of simulation variables[Vi] (i.e. simulation parameters) 1221 may include, but is not limitedto, the wind direction (varying from 0 to 360 degrees, at 1 to 45 degreeresolution), the wind speed (from 0 to 50 m/s, at 0.5 to 5 m/sresolution), the air pressure (+−150 mbar around the predicted nominalpressure at the altitude of the site, at 1 to 20 mbar resolution), airand soil temperature (+70-90 C, at 1 to 20 C resolution), potentialemission sources (their location and number is specified by theequipment or sector to be monitored), source flux (from 0.01 g/s to 500g/s, with resolutions from 0.01 g/s to 100 g/s), surface concentrations(in the case of diffuse sources, from 0 ppm to 10%, with resolutions of0.01 ppm to 100 ppm), hygrometry (0 to 100%, from 1% to 20% resolution)or boundary layers altitude, if necessary. The number of simulations maytherefore be high due to the dimensionality of the variable space andthe resolution at which these simulations may be taken and may becarefully selected based on the site's specifics. For example, it may beunnecessary to run a reference direct simulation using temperaturesunder −15C if the site's lowest recorded temperature is −15 C.Furthermore, some parameters can be ignored if their variation does notfundamentally affect the transport result.

Each of the simulation variable combinations selected is used togenerate the initial conditions (IC) and boundary conditions (BC) of asingle reference simulation in step 1210. This step is repeated for eachavailable variable combination. The initial conditions may includesetting the sites temperature and pressure and initial turbulencepattern within the simulation domain. The active sources and theiremission flux may be specified on the appropriate boundaries, and thewind conditions may be set on the simulation mesh external boundaries.The simulation of the digital twin 1230 under these conditions 1231 maybe executed in step 1212.

In step 1212, a transport simulation is performed. Flux over the site issimulated by an appropriate closure of the Navier-Stokes function, forexample, using a Large Eddy Simulation (LES) model which may be staticor dynamic, and the transport is ruled by an advection-diffusion model.Simpler or more complex simulation models may be used here as long asthe fidelity of that model is sufficient for the appropriate sourceallocation and emission flux quantification within the site operatorrequirements. The effect of gravity and earth's rotation may beconsidered when appropriate, that is, when the size of the simulatedsite calls for it. In other words, simulation parameters may alsoinclude effect of gravity and earth's rotation. The result of thesimulation is a series of fields (i.e., virtual emissions measurements),static or over time, that describe the evolution or the steady-state ofthe concentration of the target compound across the site and inparticular at the location of the virtual sensing system, as well asother flux and transport parameters. These results 1232 are accumulatedfor all the combinations of simulation parameters.

In particular, in step 1213, the weather conditions and theconcentrations of target gases can be extracted from these simulationsand directly related to the simulation variables related to sources andemissions flux. That creates a set of relationships between thepotential emission sources and sensor (It may be noted that the termsensor and air quality monitor may have been used interchangeable inthis disclosure) for certain weather conditions observed at the sensorlocation. A subset of these may be selected based on a set ofmeasurements 1223 collected on the basis of real samples. In particular,the weather conditions measured from the site in a period of interestmay be compared to the simulated weather conditions. For example, threesamples may have been taken at 25, 24 and 25 C, and with wind speeds of1, 3, 5 m/s and wind directions of 12 degrees, 24 degrees and 15 degreesrespectively. It may be therefore possible to not consider othercombinations of temperature, wind direction and wind speed from thereference set of simulations. Then, it may be possible that theresolution of the reference set does not match the exact conditionmonitored, in which case, the results may be interpolated acrossmultiple reference simulations. For example, the sample at 24 C, 3 m/sand 24 degrees may not have been simulated, but a case at 24 C, 4 m/sand 24 degrees and one at 24 C, 2 m/s and 24 degrees were. The resultsin terms of concentrations may then be interpolated (i.e. correlated) toget a composite relationship between potential emissions sources,emission flux or surface concentrations (for diffuse sources) andsensors at 3 m/s. Additionally, multiple samples may occur in the sameweather conditions, in which case, the sample concentrations may beaveraged over the various observations in the same weather conditions,or any other appropriate weighed associative or multiplicativecombination.

This process allows the creation of a set of relationships betweenvirtual sources, prescribed emission fluxes or surface concentrations,and compound concentration measurements at the virtual sensor in weatherconditions that matches the set of sample measurements [tx] of the realsensor over a period of interest. This relationship may be stored in adirect matrix 1233. The purpose of the inverse method is to identify theinverse relationship between source and sensor; namely, given certainweather conditions measured at the sensor, this method predicts theconfiguration of emitting sources and source flux/surface concentrationfrom the measured concentration at the sensor during a period ofinterest. The direct matrix 1233 then needs to be inverted. In general,the matrix 1233 is an injection and may be ill-posed (generally due torank deficiency); as a result, an inverse generation method may benecessary to inverse the matrix in step 1214. The most trivial method isto use the Moore-Penrose generalized inverse, which pads therelationship with zero eigenvalues, but any suitable inversion methodmay be used, in particular methods that specify more complex eigenvalueestimates; for instance, by complementing the solution space byappropriate fits that may minimize various norms or byconstitutive-based approaches based on the nature of the equations usedto calculate the transport problem. Regularized generalized inversestrategies may also be taken.

The end result of that inversion process is an inverse relation matrix1234 that predicts the sources and their emissions or surfaceconcentrations from a certain concentration observed at the sensor andgiven certain weather conditions. The inverse matrix may be evaluatedfor quality using its conditioning number as an error indicator or anyrelevant matrix invariant. In case of a bad condition number orequivalent error indicator, the condition number may be improved byvarying the dimensionality of either the image space or the initialdomain. In practice, this means that a larger number of observations maybe chosen or that potential sources may be eliminated from the search inthe hope of improving the well-posedness of the problem. For example, ifthe wind direction does not shift enough during the inspected period toobserve all the potential sources of emissions, it makes sense to eithereliminate from the relation matrix the sources that have no chance to bedetected by the sensor (a source north of the sensor may not have aplume detected by the sensor if the wind is predominantly from the eastin the sampling period), or by adding additional samples from anextended testing period where a sufficient number of wind directions aresampled to detect plume from all the potential sources of emissions.

A follow-up method for improving the quality of the inverse may alsoarise from regularized or tailored padding inverse generation methods,where the regularization or padding parameters may be explored in orderto minimize the error indicator associated with the inverse matrix 1234,thereby improving the prediction accuracy of the method. Once asatisfactory inverse relation matrix 1234 is obtained, a regularizedidentification method 1215 may be attempted. The trivial operation is toperform a matrix vector operation where the matrix is the relationmatrix 1234 and the vector is the set of concentration or concentrationaverages as observed by the sensor in the sample period of interest. Ifboth the model and the detection instrument were perfect, this methodcould be applied straight away. However, the model and instrument aresubject to error, which is generally greatly amplified by inversemethods. It may be therefore advantageous to regularize the inversionmethod. This may be done by adding constraints to the matrix operation,akin to a minimization, where the weight of the constraints may betailored to optimize the result. For example, one potential constraintis to force values to remain close to the mean. Other methods mayintroduce ad hoc information about the error bounds of the instrument.Other strategies may be used for regularization for that purpose.

The weight of the regularization may be obtained using tools such as anL-curve optimization. In general, one may use similar inverse methods asthe ones used for MRI image generation or seismic mapping. The result ofthe regularized identification method 1215 is a prediction of thesources that are actually emitting the target compound and an estimation1235 of their emission rates for both point and diffuse sources and/ortheir surface concentration in the case of diffuse sources. Parallel tothis process is an uncertainty quantification process 1216 which mayprovide an error estimation 1236 on both source identification andemission flux/surface concentration. The initial way to generate sucherror estimation is the estimated errors associated with the inversionmatrix (through the intermediary of its condition number) and the upperbound of the measurement error (given by the sensor system accuracy andprecision in both gas concentration and weather measurements). Thiserror upper bound can be propagated to the result and used as atolerance in the final result. For example, if the sensor system has a 1ppm precision and accuracy, the surface concentration at the source isestimated at plus or minus 2 ppm at best. Furthermore, if the couplingis weak, say if a 10 ppm concentration at the source results in a 1 ppmconcentration at the sensor for some particular transport conditions,the error at the source surface concentration is at best of plus orminus 20 ppm for the same sensor precision and accuracy. Similar errorestimation may be given to propagate the transport model error on theend result.

Another strategy for uncertainty quantification 1216 is to usestatistical or Bayesian inference throughout the process. That is,rather than solving the problem for a set of deterministic variables,probability distribution functions are used for these variables toindicate uncertainty; this results in a propagated uncertaintythroughout the process which can then be used for error estimation. Oneadvantage of this is that bottlenecks in the method may be identifiedthrough this approach such that they can be addressed either byimproving the model or sensor system, or inversely by limitingcomputational or experimental efforts. For example, it may beunnecessary to simulate cases with wind direction at a resolution of 1degree if the wind vane is only precise at 3 degrees. The uncertaintypropagation can be tailored such that the uncertainty remains uniformacross the solution space. Another advantage is that the probability ofidentifying a source may be extracted from that uncertaintyquantification, and a percentage of source identification accuracy maybe given.

Fundamentally, the method in FIGS. 13A-B is similar to the one given inFIG. 12 . In particular, steps 1319, 1320, 1321 and 1322 as well as data1335, 1349, 1350, 1351 and 1352 have similar description to steps 1213,1214, 1215 and 1216 as well as data 1223, 1233, 1234, 1235 and 1236,respectively. The principal difference between the method of FIG. 12 andFIGS. 13A-B is that two distinct inverse methods are solved in themethod of FIGS. 13A-B, namely that the weather conditions are firstmatched with an inverse problem and then the transport problem issolved, rather than a single inverse problem as in FIG. 12 . This may beof interest when more complex weather conditions need to be simulatedfor accuracy purposes; for example, in a case where the wind is dynamicand not static during the sampling. In this case, it may becomputationally intractable to simulate all the potential weatherconditions, such that an inverse problem may be solved to identify theweather initial and boundary conditions that match the observed weatherpattern during and preceding the air sample.

In the step 1310 a set of initial and boundary conditions is constructedfrom weather variables [Wk] 1331. These variables may be defined byfirst processing similarly to in FIG. 12 , by using an interpolation ofthe average weather conditions at the sensor to derive the averageweather condition at the boundary of the domain. This may provide arestricted domain to identify the dynamics of the weather conditionaround this average. The weather variables may be formed as a time basisover the duration of a particular sample. For example, the wind speedmay be decomposed over time into a set of test functions that span thetime domain such that wind speed variations during the sample may beaccounted for. This may be done for all the weather variables and a setof initial conditions and boundary conditions is found 1341. This set isused, together with the digital twin 1340 to perform a direct flowsimulation in step 1312.

These direct simulations may be dynamic simulations over the timepreceding and during a sample such as the flow of all the air parcelscontributing to the sample are represented. The result 1342 is given asa flow field over the domain, in particular at the location of thesensor. A direct relation matrix is then generated in step 1313 fromthis which relates the initial and boundary conditions of the domain andthe observed flow and weather conditions at the sensor 1343. An inversegeneration method 1314 may be used to form an inverse relation matrix1344 which relates the weather measurements at the sensor with theinitial and boundary conditions. A regularized identification method1315, similar to the one described in step 1215, as well as anuncertainty quantification method 1316, similar to 1216 of FIG. 12 , isthen performed to identify the initial and boundary conditions that ledto the weather measurement of the sample t 1345, as well as errorestimation 1346 on the quality of that boundary identification.

A set of direct transport simulations 1318 can then be run for thatsample t. Initial and boundary conditions of the sample t 1345 are usedtogether with source initial and boundary condition 1347 for thesesimulations. Indeed, a set of emission variables [Ej] 1334 is formed totest all the potential sources, their emission flux and surface sourceconcentrations that may contribute to the concentration of the targetcompound at the location of the virtual sensor in the model under flowconditions 1345. Sources and emissions are used to generate the initialconditions and boundary conditions associated with a certain sourcedistribution in 1317. The emission initial and boundary conditions 1347are then used in the transport model simulation in step 1318.

Step 1318 is repeated until all the simulations associated with each setof combinations of the emission variables are completed. This process(1313-1318) is repeated for each sample t of a certain period ofinterest. The sets of all transport results for all combinations of [Ej]for each estimated boundary conditions for obtaining the weathermeasurements at sample tin a set of interest [tx] is given in 1348. Theprocess in steps 1319 to 1322 is then similar to steps 1213 to 1216 inFIG. 12 , namely that the relationship between source, emissions, andsensor concentrations for that time period of interest is obtained(1319,1349), that relation matrix is then inverted (1320, 1350), and anidentification method 1321 and uncertainty quantification 1322 areperformed to identify sources, their emission flux or surfaceconcentrations 1351 and their error bounds 1352. Note that theuncertainty quantification 1322 of FIGS. 13A-B have the specificity ofbeing propagated from the error estimation 1346 evaluated for thespecified boundary conditions.

Note that the methods described in FIG. 12 and FIGS. 13A-B may also beused in multiple target gas identification methods. In these cases, notonly the source identification, their flux and or surface concentrationsare sought, but as well their compositions with respect to the targetgases. For instance, it may be possible to track both methane andpropane at an oil and gas site, and different potential sources may emitdifferent composition ratios, for example, the liquid tank emissions maycontain a much larger fraction of propane than methane, when comparedwith a wellhead emission.

The methods presented in FIG. 12 and FIGS. 13A-B are possibleembodiments to localize and quantify the emissions and their sources.Possible embodiments for the qualification of source with respect totheir type is presented in FIGS. 14A and 14B. FIG. 14A presents aflowchart 1400A for construction of a statistical inference method wherethe emission type is distinguished from another by their characteristicsin terms of composition, frequency or duration and intensity. This isparticularly important to distinguish between allowed emissions andleaks since some equipment or activities may emit the target compoundsas part of their normal operating process. One potential first step maybe to create an estimation of the probability density functions ofequipment emissions 1410. This can be done by collecting detaileddescriptions of the equipment behavior currently deployed at a site1421, which may include information or estimations about the emissionfrequency, intensity, and composition in normal operating conditions.This may be obtained from the site operator or from direct observationof the equipment type and using manufacturer- or industry-specificinformation to aggregate the emission frequency, composition, andintensity for the deployed equipment. Another source of information maybe equipment reference measurements 1422, either for each type ofequipment for a period of time or for the whole site to be observed,when the operations are supposed nominal (i.e., no leaks). This may bedone, for instance, just after installing the monitoring equipment byfirst completing a full maintenance of the equipment for nominaloperations and by observing the subsequent equipment reference behaviorusing the sensor system described herein in order to generate a set ofreference measurements 1422. These measurements are then analyzed usingappropriate statistical methods to extract the expected equipmentemission frequency, intensity, and composition in normal operations.Similarly, specific failure modes resulting in fugitive emission mayalso be qualified if at all possible. For example, a stuck open valvemay be voluntarily simulated to evaluate its emission profile if such anevent did happen by accident. The result of this analysis is a set ofstatistical data characterized by probability density functions for theemission frequency, duration, intensity, and compositions 1430.

The characteristics of the sensor measurement 1423 are considered instep 1411 by the computation of conditional probabilities. Indeed, basedon the sensor system placement, frequency of measurement of a certainequipment group, accuracy and precision of emission intensity, durationof sample and so on, the conditional probability of the observation ofan emission given the characteristics and limitations of the sensorsystem is calculated from each equipment probability distributionfunction 1430. The conditional probabilities of equipment emissions 1431may be used to generate composite conditional probabilities for eachsource in step 1412.

Indeed, a source may contain multiple equipment types. This may begenerated as a composite of the equipment emissions conditionalprobabilities if a list of equipment for each source 1420 is obtained,or it can be constructed directly through the process 1410-1411 ifsource reference measurements as a whole 1424 are given. This leads tosource emission conditional probabilities 1432 as observable by thesensor system. In step 1413, these probabilities may be used to qualifythe emission types of various observed emissions from the source 1425,obtained by the sensor system. The conditional probability could be lessadequate over time due to a plurality of factors such as weatherconditions, seasonal changes, and operational changes at the well andshall be considered in a differential manner; that is, the record ofpast emissions events 1426 from that source may be used to continuouslyrefine and update the initial conditional probabilities such that thenumber of false positives are minimized. In practice, the probability ofan emission as observed by the source over a period of time iscalculated from these conditional probabilities 1433. For example, in anatural gas upstream onshore site, a liquid unloading event may beidentified when a high intensity, short duration and low frequency eventinvolving mostly liquefiable hydrocarbons occurs; this may bedistinguished from high frequency, short duration low intensity eventssuch as methane puffs from pneumatic controllers. The probabilitiesobtained 1433 are analyzed and ranked based on likelihood. Emissionevents with high probabilities to be identified as a specific event orequipment type are sorted. Unsortable events which are unlikely to benormal equipment emissions may be identified as outliers. If suchoutliers have high intensities, these may be identified as fugitiveleaks. An embodiment of a certain unqualified fugitive leakidentification method through a graph 1400B is illustrated in FIG. 14B.In FIG. 14B, consecutive measurement averages as observed from aparticular source are compared after a specific peak event is detected.The curve 1442 denotes the limit under which 50% of the nominalemissions are typically observed. Note that on small time intervals, theemission intensity average may be high, because it is dominated bynormal high-intensity low-frequency events, noted by 1441, which mayoccur, such as liquid unloading. At long average periods, the curve 1442tends toward the site long term nominal emission average. 1440 denotesthe contributions from equipment that emits at low intensity but at highfrequency, as exemplified by their large contribution to the siteoverall average over time. The curve 1443 denotes the limit at 95%, forwhich 95% of the observed normal emissions are under the curve. Thus,these curves may be used to identify the outliers. For instance, notethat 1444 is under the 50th percentile and may likely be a normalemission. Furthermore, its high intensity makes it a likely contenderfor a high-intensity, low-frequency event of the type depicted by 1441.On the other hand, measurement average 1445 is outside the 95 percentilerange, it may therefore be a fugitive emission, even if 1445 intensityaverage is lower than 1444. This method may be used to qualify theleaks. Note that the probability of some low-frequency event maycollapse. For example, if only liquid unloadings are responsible for1441, and if a liquid unloading occurred the week prior, the probabilityof a new liquid unloading this week is extremely low, which would makecurve 1441 collapse and reduce the intensity of 1442 and 1443. In thiscase, 1444 may be considered to be a fugitive emission. It is thereforeimportant to keep track of infrequent events in order to adapt theconditional probability to the specifics of a certain site's activity.Note that in general, leaks are either continuous or intermittent andcan be generally identified in outlying deviations of the long termaverage, but early detection may be of interest as well. The embodimentof FIG. 14B may be supplemented with deviation estimates such as thecalculation of a weighted integral of the evolution of observationsaverages over time and comparison of this metric to the 95% average.

Beside the detection, localization, quantification, and qualification ofthe emissions of a site for certain target compounds, specific metricsof interest may be considered such as total site emissions. A method ofcomputation of such emissions is proposed herein.

Assume that detection, localization, quantification, and qualificationof emission has been performed and that emissions are characterized forthe site over contiguous time periods where both emission and weatherhave been measured and calculated.

In the case of point source and diffuse sources where the emission fluxis known, this may take the form of average emission flux for all theemitting sources on the site over periods T, following each other. Theaverage total emission of a site may simply be calculated as the sum ofeach emission flux for all point sources, which may be weighted by theestimated start time of each source emission, interpreted based on theirprobability of intermittency and smoothed over time. This process mayprovide a total emission estimation from one period to the next.Interpolation considering diurnal effects and seasonality may be usedfor padding the total flux estimate when measurements were unavailable,and total emission flux for periods of interest such as a week, a month,a quarter, a year or so on may be evaluated.

In the case of some diffuse sources, the emission flux may not be knowndirectly, and surface concentrations may be known instead. This may bethe case for sources at low pressure and high reservoir, where theemission flux actually depends on the transport rate. Assume that thesurface concentration of a diffuse source across multiple sectors isknown as an average for a certain time period and that weatherconditions are known in its vicinity. A direct transport simulation ofthe digital twin of the site may be performed using part of the methoddescribed in FIG. 12 and FIGS. 13A-B. This simulation may be run withboundary conditions that match the weather conditions measured duringthe time period as well as the surface concentration as measured on thesource surface. The direct simulation is then conducted for all theweather conditions as measured during the sample period and the outboundemission flux is calculated at virtual surfaces enclosing the source.Indeed, the flux may be calculated this way by measuring theconcentration and area of all the virtual surfaces enclosing the sourceand multiplying it by the normal to the surface component of the localvelocity field. This is equivalent to the mass conservation methodemployed for plume cross-sectional computation of flux but is conductedon the simulated digital twin. As a result, the flux of emissions may beknown over time for a diffuse source from ground concentrations and windmeasurements using a simulation model. Note that this method may beapplied with measurements of the ground concentrations of the source,rather than calculated by an inverse method as in FIGS. 12 and 13 .Indeed, this may be a practical methodology for landfill total emissionestimation as landfill surface concentration measurements are routinelyconducted to identify hot spots. The further use of wind measurement canthen complement the measurement of surface concentrations, together witha digital twin, to provide a novel method for total landfill emissioncalculation.

One particularity associated with the use of a static sensor system forthe detection of a particular emission is that the detection thresholdof an emission may vary greatly based on external factors related to thetransport of that emission from the source to the sensor. For example, asource hundreds of meters away from the sensor may be easily detected ifa direct path and frequent weather pattern lead to the transport of theemission plume to the sensor, while a source of equivalent intensity,mere meters away, may never be detectable due to an impassable obstacle.Source interference may also be at play, for example when two sourcesare not separable from each other for being too close together or frompresenting a similar angle of view from the perspective of the sensor,the closer source thereby partially occulting the more distant source.These considerations are schematically approached in FIGS. 15A-15D,representing in turn an idealized sensor 1520 and its detection limit1521, the effect of local wind as exemplified by a wind rose 1523, theeffect of topology as depicted by the isocline 1522 and the effect ofocclusion by an undesirable source 1524, respectively.

In FIG. 15A, a view of a schematic map of a sensor system 1520 is given.In this example, the topology is supposed to be flat, with norheological effect from the ground. If the sensor detection limit issupposed constant and the weather uniform with wind directionequiprobable, the intersection of the detection limit of a source with aplan parallel to the ground is a circle. If the wind has no altitudeterm (normal to the ground), then the three dimensional view of thatlimit is akin to a cardioid centered on the sensor. In practice, thedetection limit may take complex form due to external factors; some ofthem are presented in FIGS. 15A-15D.

In FIG. 15B for instance, an average wind rose diagram 1523 related towind speed for a period of interest is given. This deforms the detectionthreshold of the sensor system, allowing for detecting emission furtheraway in the direction of faster and recurring wind. This is because ofthe relative weight of diffusion and advection on the transport of theemission, and faster wind increasing the distance at which a highconcentration may be observed. Similarly, repeated observation in thesame wind direction may reduce the effect of the noise of the sensing byaveraging and decreasing the detection threshold of the system. Theresult is a non-uniform detection threshold curve.

The effect of terrain and topography 1522 on transport is evaluated inFIG. 15C. As mentioned earlier in this disclosure, wind patterns mayavoid obstacles which give rise to curved streamlines. If a topologicalfeature 1522 lies in the detection area of a sensor system, thisdetection limit may follow the weather pattern topology and likewiseshape itself following average streamlines. Further, in FIG. 15D, source1524 may cause obstruction that limits the detection threshold in itsvicinity. This can occur when one observed source blocks the detectionof another source of interest in the same angular region, when twosources are not distinguishable because of being too close to eachother, or when undesirable sources are interacting with the sensor. InFIG. 15D, an undesirable source 1524 is located within the detectionarea of the sensor. The detection area is then sharply reduced in thevicinity of that source because a source in the vicinity of source 1524may be confused for source 1524.

Such consideration as presented in FIGS. 15A-15D may be taken intoconsideration when selecting placement of the sensor within a site andoptimization of sensor networks which maximize detection whileminimizing the overlap of sensor detection areas. Note that while onemay use the concepts presented in FIGS. 15A-15D and other adjacentconcepts to create ad hoc rules for sensor system positioning, this mayonly be performed in a qualitative manner. In order to effectivelyevaluate the practical detection area of a sensor, one may use anexperimental or modeling approach. The experimental approach optimizesthe positioning of the sensor by comparing expectations in detection toactual detection in the field, therefore effectively measuring theposition of the detection threshold. This may be done using thepotential sources themselves to generate data or by using a tracercorrelation method. Another method relies on simulation to provideadequate information and estimation of the shape and size of thedetection threshold. To employ such a method, one may use the directtransport model over a digital twin of the prospective site. Thedetection threshold may be found by testing the source-to-sensorcoupling virtually by providing simulated test sources at variouspositions and distances from the sensor, therefore fully characterizingthe detection area. Another simulation technique may only use thesources of interest and verifies that each potential source is locatedwithin the detection area of the sensor by simulating virtual leaks fromeach source at the wanted flux or surface concentration threshold. Thismay also be conducted at the network level, that is for large siteswhich may require more than one sensor.

Using this strategy, the detection area of a sensor may be described andfully utilized to the limit of the sensor system, thereby reducing thenumber of sensor systems to be deployed and maximizing coverage. In someembodiments, detection speed is also of interest, in which caseredundancy of coverage from multiple sensors may be used to maximize thespeed of detection. Indeed, wind direction may shift during observationand every detection that should occur faster than the characteristictime necessary for the wind to cover most directions may require morethan one sensor in order to be detected in time. This requirement may beadded to an optimizing network algorithm running the direct transportsimulation. The positioning of the sensor may be adjusted in thissimulation to provide maximized coverage at the necessary detectionthreshold and detection speed. This optimization may be performed by arandom search (e.g., Monte Carlo method) of the space of positions forthe sensor in which a minimum is sought that reduces the number ofsensors and increases coverage. Other directed algorithms may be used,such as genetic algorithms or gradient-based algorithms to identifyconfigurational minima. Machine learning, neural networks and otherAI-based approaches may be used to provide adequate initial guesses toaccelerate this optimization. Human experience may also be used for aninitial guess.

The objective function that governs this optimization may be defined insuccess/failure metrics or by progressive scores such as measurementover detection thresholds and detection speed over desired detectionspeed. Measurement over detection threshold ratio may be optimized to besuperior to 1 and detection speed over desired detection speed may beoptimized to be inferior to 1. A minimum-maximum optimization is thenperformed to maximize the realization of the objective function whileminimizing the number of sensor systems used.

The number of sensors and their position can then be chosen for the siteby selecting the best optimization result with a sufficient margin ofsafety to guarantee operation over time.

FIGS. 16A-16B illustrate symbolic maps 1600A and 1600B of sensor networkdeployments for a diffuse source area and for point sources,respectively. The symbolic map 1600A of FIG. 16A is constituted of twosensor networks, for a large diffuse source akin to a landfill. Thesymbolic map 1600B of FIG. 16B is for a site with multiple pointsources, akin to an onshore natural gas field with multiple well pads.In both cases the wind speed and direction distribution are given by thewind speed rose 1623.

FIG. 16A shows four sensors 1620 deployed on a mound that is akin to alandfill. The isoclines 1622 denote the altitude change and the greyedarea 1621 the diffuse sources. The dotted line 1630 indicates thedetection threshold of the sensor 1620. It should be noted that aquasi-total coverage of the diffuse source is realized by the sensorplacement choice and that the area of detection of each sensor isinfluenced by both the wind pattern and the topography of the land. Sucha complex detection area may not be easy to define without a digitaltwin simulation without redundant coverage necessitating more sensorsystems to be deployed. The diffuse source may further be divided intosectors that have equivalent emission contribution to each sensor andconsider sensor area detection overlap.

FIG. 16B illustrates the deployment of sensors in a field with manysources (indicated by solid black circles) 1624 and three sensors(indicated by white hollow squares) 1620, 1625, 1626. Sensor 1620detection area (dash-dot line surrounding 1620) is mainly influenced bythe wind pattern, while sensors 1625 and 1626 are also influenced by thetopology noted by the isocline 1622. The grey line 1631 denotes thehypothetical detection area of the sensor 1626 if the topology was notconsidered. Note that the source 1628 is not actually contained in thedetection area of any sensor even though 1626 could hypotheticallydetect source 1628 were topology 1622 not present. Similarly, source1627 may be detected by either of the two sensors; in practice, 1627 maybe partially occulted by another source from the point of view of sensorsystem 1626. The coverage of the source 1627 by the sensor system 1625provides distinguishable coverage of 1627.

The examples of FIGS. 16A and 16B exemplify the need for optimization inthe deployment of a sensor network and the need for a fine understandingof the effect of external variables on the behavior of emissiontransport. The methods presented herein cover both conceptual,experimental and simulation approaches to optimize a network of deployedsensors that monitor emissions in real time. In some embodiments, thesystems can include an air quality monitoring system and/or othersystems or components disclosed herein. FIG. 16A illustrates the systemincluding the air quality monitoring system 110 discussed in connectionwith FIG. 1 and can be programmed to receive output from the sensors1620, 1625, 1626 via wireless, wired, and/or optical connections.

One embodiment of the disclosure concerns a method to generate emissionpredictions, preventative maintenance predictions, and targetedequipment and process replacement from existing data streams that may beinterpreted to quantify, qualify, localize, and reduce emissions.Another embodiment of the technology can be a hybrid inspection methodthat may involve additional sensing modalities beyond static, real-timesensors, namely, fence-line monitoring, operator-based, drone-based,plane-based, or satellite-based systems that may or may not be used inconjunction with stationary sensing.

Another embodiment of the technology is a method for the monetization ofemission reduction by taking advantage of financial markets. The systemscan be programmed to identify emissions, track emissions (e.g., trackfor emissions credits/compliance), and manage emissions by controllingequipment, generating schedules (e.g., operation schedules), etc. Forexample, the system of FIG. 16 can be programmed to monetize emissionreductions based on one or more monetization algorithms. Thecalibrations and source determination techniques of FIGS. 3-5 can beused to calibrate the system. Sensor deployment techniques discussed inconnection with FIG. 7C can be used to select the number of sensors,sensor position, etc.

For the first method, existing data records regarding site operations,external or internal reporting, raw inspection data and other datasources can be used to inform emission quantification, qualification,localization, and reduction.

Existing data streams may come from existing processes andinfrastructures that are related to the normal operation of the site.For example, an oil and gas-producing site may collect operational datarelated to product quality, operating pressures, volumes, temperatureand other product characteristics, actuation of actuators, and so on. Inanother example, the landfill industry may record waste volume,compaction of landfill layers, landfill gas composition, landfill gascollection system pressure, and so on.

External or internal reporting may also generate data sources such as arecord of past emissions, current equipment inventory, maintenancereports, or other reports, as needed or appropriate. For example, if afederal environmental agency mandates new oil and gas sites to reportthe equipment types present on the site, the system can estimate thenormal emissions and leaks observed during inspections.

Raw inspection data taken from mandatory or voluntary inspections mayalso be mined for emissions data. Indeed, mandatory, and voluntaryinspections may generate raw data that can create value beyond theircurrent usage for inspection purposes. For example, in both the oil andgas and landfill industries, only concentrations higher than a certainthreshold are reported to environmental agencies even though technicianscollect raw data continuously throughout the site, regardless of whetherit is above or below a threshold.

Additional external data sources may be used, such as the EPA emissionsinventory which lists the average emission volume of target compoundslike methane for each equipment type.

The data stream from these various data sources may be organized andanalyzed.

First, the data may be organized into databases that describe each siteand list the site equipment inventory, regulatory declaration of normalemission for a period of interest, regulatory declaration of emissionsdue to leaks, number and volume of leaks identified during inspections,and so on. An analysis may be performed to rank sites based on theiremissions. Many sites may be compared, and outliers may be identified bycomparing recent regulatory declarations of emissions compared to siteemissions inventories, which may lead to identifying sites with higheremissions.

Second, timelines may be created for each site that may include thetime-dependent operational data from each site, such as productionvolume, pressure, other measured product characteristics, externalactuation settings and so on, as well as other data streams related tomaintenance and reporting such as operator reports, part replacement,schedules for product unloading, repair due to leaks identified outsidemandated inspection, site visits and so on. Each of the variablesassociated with an event may be quantified numerically and added to thetimeline. Derived variables or events may be constructed by identifyingthe patterns in each variable timeline. For example, the event“transient overpressure” may be associated with an elevated pressure inthe pressure measurement of a process.

The timelines generated for each site may then be analyzed to identifycorrelations between events emanating from different data streams. Forinstance, a statistical analysis may be performed by generating across-correlation matrix to identify correlations between events. Forexample, a large leak repair identified in the maintenance report may becorrelated with a loss of product pressure from the operationalvariables.

The timelines of each site may be analyzed across sites. For instance,statistical trends may be established by comparing thecross-correlations of events happening at many sites. The results ofthis analysis may be the identification of repeat failure for certaintypes of equipment and the prediction of failure by the observation ofcorrelation between failure and atypical operational measurements suchas production volume, composition, pressure, temperature and so on. Theanalysis may further correlate elevated operational emissions withoperational practices, such as excessive venting due to uncontrolledpressure in a controller PID. Such analysis may be performed usingstatistical tools or using artificial intelligence algorithms such asclassification algorithms to create separate event categories andmachine learning to identify correlations. The machine learningalgorithm may be trained on validated data by operators that understandthe causal relationship between process variables and emission behavior.Centralized computing units and databases (e.g., centralized computingunit 727) can store algorithms, models, lookup tables, and other data.

Third, raw inspection data may be analyzed. This may include theanalysis proposed with the static sensor from one illustrativeconfiguration described or other sensing strategies. Some sensors usedin the inspection may produce concentration measurements or emissionflux measurements in the vicinity of various equipment. Some also logthe gps coordinates of the measurement or the equipment that wasmeasured. This concentration or emission flux list, together withweather data—from the weather agency at the location of the site at thedate of the inspection, or from local weather measurement—may be used toplot a concentration map or emission flux map of the site. Theconcentration or emission flux map may be formed by localizing eachmeasurement on the site and interpolating the measurements at positionswhere no measurement was made. Depending on the nature of the site, theconcentration or emission flux map may be only defined on the sources orin the direct vicinity of the sources. This concentration informationmay be used to run a simulation of the forward transport of the speciesconcentration by specifying boundary and initial conditions that matchthe weather conditions of the day of the measurement. The transportsimulation result is further interpreted to evaluate an estimation ofthe total emission rate of the site on the day of the measurement. Thismay be further extrapolated in time, by assuming constant emission andby varying the weather conditions to match later dates. The emissionsmay be inconsistent, and the sources may be adjusted using various datasources, in particular, the operational and maintenance data timelinespresented herein, as well as subsequent inspection or other sources ofmeasurement. Indeed, the method presented herein proposes a continuouslyupdated digital twin of the site's emissions using data streams fromvarious sources to intermittently reassess the emissions profile. Suchprocesses may lead to improving the total emissions estimate of a sitein the absence of high-frequency or real-time measurements.

This approach is for instance very fruitful for the case of landfillswhere the mandated inspection measures surface concentrations on theentirety of the site. The mandated inspection only mandates the reportof concentrations higher than an allowable threshold, above whichmaintenance of the landfill cover is necessitated. However, the rawinspection data may contain concentrations for all measurement points.Using this data together with wind data from the landfill is a novelmethod for the prediction of total landfill emissions.

Sites may be compared and the datastream analysis methods proposedherein for reporting data, operational data, and inspection data may beused concurrently to (1) Evaluate total emissions, (2) Identifyhigher-emission sites, (3) Identify systematically failing components orequipment types, (4) Identify operational practices or equipment leadingto higher emission, (5) Predict failures from operational measurements,(6) Identify mismatch between reported emissions and predictedemissions, (7) Identify best practices and equipment types limitingemissions, (8) Identify lower emission sites, and/or (9) Identifylow-maintenance components or equipment types. The systems and methodsdisclosed herein can be configured to provide one or more of (1)-(9)discussed above.

This information may be used to provide actionable insight to theoperators. In particular, the evaluation of total emissions may be usedto improve the accuracy of reporting. Higher-emission sites may beselected for higher-frequency inspection and maintenance. Systematicallyfailing components may be phased out to limit maintenance cost.Operational practices and equipment with higher emissions may bereplaced for reduction of emissions. The prediction of failure may beused to trigger preventative maintenance to avoid downtime and reduceemissions. Emissions reports may be revised if reports are related to aregulatory risk where reporting accuracy is later judged by inspections.Best practices and equipment type reducing emissions may be extended intheir scope to reduce emissions and maintenance cost. Lower-emissionssites may be inspected less frequently, thus reducing inspection budget.Low-maintenance components may have an extended use to limit maintenanceacross the sites.

The quality of the information obtained by existing datastream analysismay be enhanced by increased inspection frequency such as through thestatic monitoring device described herein and may justify the usage ofstatic monitoring for some sites. However, real-time monitoring may notbe the most cost-effective method for inspection for all the sites or atall times. A method for dynamically selecting the most effectiveinspection method based on the datastream described above is presentedherein. Other inspection methods such as operator-based, drone-based,plane-based, satellite-based or fence line monitoring may be usedtogether with continuous monitoring from static sensors to provide aholistic approach to monitoring. Indeed, some sites may havetopological, environmental, technical and/or economic criteria thatwould make a particular embodiment of a compound monitoring system moreworthwhile from an emission reduction perspective at a certain time. Forexample, densely packed oil and gas production sites that produce largevolumes, as well as compressor stations, tank batteries or otherconcentrated sites with a large number of potentially emitting sources,may be ideal for continuous or close to continuous monitoring; whileremote, sparsely located, low production volume sites may gain frombeing monitored less frequently by aerial inspection. Similarly, theemission risk over the life of the equipment may change significantlyand as a result the optimal inspection strategy may change over time.Finally, the overlay of different inspection methodologies may changethe inspection requirements of a site based on the availability ofinformation about emission at a certain time and the rapidity with whicha particular inspection embodiment can be deployed.

The technology can dynamically blend different data-sensingmethodologies to provide a hybrid method which may utilize more of theadvantages of multiple embodiments of disparate systems for themeasurement, quantification, localization, qualification of emission ofcertain compounds as well as for the reduction of such emissions, allwhile optimizing for capital utilization. Different types of sensors canbe used on a site. The number of sensors, sensor functionalities, and/orsensor configurations can be selected based on the sensor locations.

In particular, the analysis from the existing datastream informs aboutwhich sites are large emitters and which sites are emitting less. If anoperator has many sites, such as in the upstream oil and gas industry,having different approaches for different sites may be a cost-effectiveemissions reduction strategy. For example, in the oil and gas upstreammarket, approximately 20% of sites may be responsible for 80% of theleaks by volume. This would suggest that the budget dedicated formonitoring, as well as the frequency of monitoring, should be highest inthis 20% of sites. These sites may be identified through the datastreams presented herein. Prevalence of failure points also influencesthe necessity of monitoring. An oil and gas site, for instance, withnumerous wells and other systems such as separation units, tanks,injection pumps and so on will have more emissions and more leaks than asite with lower equipment counts. The average number of failures orleaks per equipment type may be predicted from a maintenance report, andthe combined number of failures or leaks per year for a site may becalculated from these equipment failures or leaks or extracted from themaintenance or leaks report data streams. In particular, frequency ofmonitoring may be set in relation to the frequency of failure or leaksof a certain site. In some cases, the frequency of monitoring may bepredicted as lower than the mandated inspection frequency, in which caseno additional monitoring may be required. In other cases, the frequencyof monitoring needed may be higher than the mandated inspectionfrequency in which case additional monitoring may be prescribed. Theschedule of that additional monitoring may be selected to minimize theuncertainty associated with the state of the equipment from the site.For instance, for a site mandated to be monitored once a year, it may benecessary to add the additional monitoring step at the six-month marksuch that monitoring inspections are equally spaced in time. Thisscheduling may be influenced by other factors such as seasonality,operational state of the site, density of neighboring site or otherfactors. For instance, monitoring for butane gas leaks in Alberta duringwintertime may not be sensible because butane does not vaporize at lowtemperatures. Another factor of interest is the intensity of the leaks.Scientific literature suggests that the emission of typical airbornecompounds of interest (e.g., methane or other compound) can generallyfollow the rule, meaning that 20% of the largest leaks emit 80% of thecompound. This means that this larger type of leak, while less common,emits more than an average-sized leak. Other rules can be determined andused. Identifying the sites or equipment with the highest probability oflarge leaks can inform the order or priority of inspection, maintenance,etc. A third factor of interest is the intermittency of leaks. Someleaks are intermittent at a certain frequency, and this informs thefrequency at which the measurement needs to be performed. A fourthfactor is the response time of the operator. Indeed, certain sites areinaccessible, and the operator may not be able to respond rapidly to aleak, in which case the rapidity of measurement may matter less than thecertainty of it. A fifth factor is the possibility of overlappinginspection methodologies. For example, one may use satellites at thefield level to inform of leaks sufficiently large to be detectable fromspace, which may dynamically trigger inspection visits to target sites,reducing the cost of monitoring a large area. A sixth factor is theproximity of various sites. Indeed, sites sufficiently close togethermay be inspected by a single static monitor, therefore amortizing theinstrument cost over multiple sites.

Leaks are not the only type of emission that may be observed,identified, and/or analyzed at a site. A large fraction of emissions canresult primarily from the activity or from the operation of theequipment. If the total emission is of importance from an inspectionstandpoint, real-time (continued or periodic) or frequent inspectionmethods may be of interest.

Externalities such as weather and remoteness of the site may influencethe best method to be used. High cloud cover can for instance blockobservation from space and harsh weather conditions and lowcommunication infrastructure can influence the cost of deployed sensors.

The return on investment for a certain inspection method may reduce withfrequency: once initial leaks are repaired, a long period of time mayelapse before new leaks occur, meaning that the probability of leaks isdependent on the history of the site and may widely vary. Thus, the useof datastream and statistical inference of the conditional probabilitiesof leaks is tremendous for the prediction of potential leaks andappropriate inspection schedules and methods. The proposed methodweights these various factors to select the most appropriate inspectionembodiment.

The advantage of each inspection method is described herein. Staticmonitoring through a single sensor or through a network of sensors mayprovide high-frequency measurements, with tailored detection thresholdsbased on the distance of the sensor to the potential source, and addressat least equipment identification, as presented in the disclosure.Because the sensor is static, the cost of the inspection is determinedby the number of potential leak points observable in the detection area,site size, and the cost of ownership of the sensor system, which may behigher than a mobile solution on a per-year basis. The advantage of amobile solution could be the possibility of amortizing the measurementprice on a larger number of potential sources to the cost of lowerfrequency and/or lower detection limit. For instance, drones may be usedonce per quarter and have a low detection limit, while satellites mayhave a 24-days frequency and cover large swaths of land but only detectthe largest leaks. Monitoring by plane falls in between the satelliteand the drone, and thus could offer a balance of price, inspectionfrequency, and detection limit. Manual site inspections oroperator-based inspections are driven by the cost of labor and avariable measurement quality depending on operator competency, but theseinspections can generally pinpoint the leak location and partiallyassess their size. Similarly, a larger firm could amortize labor costsacross many sites, whereas smaller firms may pay more in labor costs persite.

In certain embodiments, methods can identify the best method or methodsfor site inspection at a given time by calculating the advantages anddisadvantages as a function of the expected site emissions volume andfrequency and the externalities associated with the measurements inorder to maximize a measured emission volume while minimizing the cost.The higher volume of measured emission may then be used to provide ahigher volume of reduced emissions.

One embodiment of the technology involves using monitoring informationand data streams to enhance product recovery and emission reduction andto generate income by emissions reduction credits, such as carboncredits or added value at the sale of the product via product labelingor certification. For example, this technology could be employed tocertify low-emission natural gas or biogas, or some other certificationor labeling of interest, in the case that the measured compound is agreenhouse gas, valuable gas, or commoditized product.

Indeed, the detection of greenhouse gases emitted during operations maybe used as a quantification of carbon-equivalent intensity. In general,carbon credits in a cap and trade market may be allocated based on thecarbon emission offset compared to competitors for a certain productintensity. For example, a certain number of carbon allocations may beprovided for a certain number of MMBtu produced in a gas field. Anoperator that emits fewer greenhouse gases and can demonstrate that factthrough emissions quantification may demonstrate emitting less per MMBtuproduced, and thereby earning carbon credits which may be sold on thecarbon market for a profit. In another embodiment, in the case of anopen carbon market, the measurement of carbon equivalent emissionsthrough the use of the method proposed herein may be presented as acarbon offset method directly by quantifying the amount of carbonequivalent reduced through the application of the method and may be soldas such. For instance, the use of a static sensor may lead to thereduction of methane emissions that if related to the cost of operationof the sensor, may lead to a significantly lower cost per carbon tonequivalent than the spot price. The reduction of the carbon footprintmay be evaluated, and the difference may be sold as a carbon offset onthe carbon market.

The other path to revenue that commoditizes emission monitoring andreduction resides in the certification of the product being produced bythe monitored equipment. Indeed, the environmental impact of thecondition in which the product is produced may impact the certificationof the product to certain standards, which in turn can be sold at ahigher price than a product that does not meet the standard. Forexample, the emissions due to the production of natural gas may reachlevels that make the greenhouse gas impact of natural gas on par withburning coal, negating its value proposition of being a moreenvironmentally friendly fuel. Some certified natural gas productsattain a price that is up to 1% to 10% higher than the non-certifiedcommodity. The monitoring of emissions and reduction of emissiondisclosed herein can help producers meet the strict rules and burden ofproof associated with certification.

In all or some embodiments, the method can include the quantification ofemissions and emission offsets obtained by a hybrid/dynamic inspection,preventative maintenance, and operational optimization for thegeneration of low emission certified products, carbon offsets, or thereduction of carbon credit consumption through emissions reductions andtotal emission reporting.

The construction and arrangement of the elements of the systems andmethods as shown in the embodiments are illustrative only. Although anumber of embodiments of the present disclosure have been described indetail, those skilled in the art who review this disclosure will readilyappreciate that many modifications are possible (e.g., variations innumber of sensors, sensor position, removal and addition of sensors,weather detection elements, etc.) without materially departing from thenovel teachings and advantages of the subject matter recited. Forexample, elements shown as integrally formed may be constructed ofmultiple parts or elements. Any embodiment or design described herein isnot necessarily to be construed as beneficial or advantageous over otherembodiments or designs. Accordingly, all such modifications are intendedto be included within the scope of the present disclosure. The order orsequence of any process or method steps, including the steps discussedin connection with the algorithms discussed herein may be varied orre-sequenced according to alternative embodiments. Other substitutions,modifications, changes, and omissions may be made in the design,operating conditions, and arrangement of the embodiments withoutdeparting from scope of the present disclosure or from the spirit of theappended claims. For example, the techniques disclosed herein can beused to monitor other locations, including inside factories, warehouses,shipping centers, homes, apartments, or the like.

The present disclosure contemplates systems and methods which may beimplemented or controlled by one or more controllers to perform theactions as described in the disclosure. For example, in someembodiments, the controller, whether part of a sensor, computing device,etc., may be configured to process data from sensors, users, oroperators and model, calculate, and perform one or more simulationswithin different data sets, tables or maps described, perform any or alldescribed algorithms and any others similarly suitable, and controloperation of any disclosed parts or components in a manner necessary orappropriate for proper function, operation, and/or performance of anydisclosed systems or methods.

1—Gaussian Plume Model: An aspect of the system may use a reduced ordermodel rather than a full dispersion advection transport model for thesimulation of transport of the trace gas of interest. In particular,Gaussian Plume modeling may be used. The Gaussian plume model uses agaussian approximation of the plume geometry to approximate dispersion.This model assumes a flat terrain and a well-mixed dispersion process.The gaussian Plume is a reduction of a steady state solution of the flowequations in this simple geometry of the terrain. Therefore, only a fewparameters are sufficient to describe the model, such as: the source tosensor distance and direction, the wind direction, the height of thesource and the height of the sensor. Internal parameters include thedispersion width in the horizontal and vertical directions through theintermediary of the standard deviation of the gaussian shape. A simplereduction consists in taking an identical standard deviation for bothvertical and horizontal terms. Some approximation of the dispersionwidth can be obtained using Pasquill curves which may depend on theatmospheric stability class at the time of transport and distancebetween source and sensor. One configuration of the present disclosureis directly estimating the stability class and or the dispersionstandard deviation using the measured standard deviation of the wind atthe sensor location on a time scale that is corresponding to the time oftransport from the sensor to the source. This standard deviation iscalculated over many samples using the wind direction change during aperiod of interest, for example using 1 sample per second over a periodof a minute to calculate the wind standard deviation. It is thenpossible to use the horizontal wind standard deviation to calculate thestability class and then use this to calculate the dispersion standarddeviation. Alternatively, the standard deviation of horizontal wind canbe used to directly approximate the plume dispersion width.

When the internal dispersion terms are obtained, the other inputs suchas concentration at the sensor, position of source and sensor andaverage direction of wind during the observation period can be used tosolve the gaussian plume equation. Note that the direct gaussian plumeequation relates flux at the source to a concentration at a selectedpoint. The inverse gaussian plume equation permits to relate theconcentration at a point to the flux at the evaluated source. Becausethe position of source and measurements at the site setup can bedetermined, and wind speed, wind direction and concentration may havebeen measured continuously, the flux of a source by using the inversegaussian equation may be estimated.

The gaussian plume model and its inverse model can be used in themethods described in FIGS. 12 and 13 as an alternative to the morecomplete dispersion advection transport model as a lower computationalcost alternative. This is to the cost of ignoring the effects oftopology and obstacles that are considered in the dispersion advectiontransport model.

Quantification Algorithm: A quantification algorithm may be used toquantify and detect leaks from the use of continuously monitoredconcentration and wind data. There are four major steps in the road mapof this algorithm: localization, event detection, backgroundcalculation, and atmospheric stability. The localization uses thelocation of the sources and detectors to calculate the probability of adetector seeing an event or leak from each sensor. Emission plumes, forexample methane plumes of equivalent size are compared along with thepeak events at each sensor. The most probable source will be identified,and the source will collapse if there is no event identified. Theprobabilities from each detector then provide a weighted average of theflux rate at each source.

During event detection, the methane plumes “seen” by the detectors areindividually isolated, so that each event can be identified. Thebackground calculation involves estimating the background concentrationfor each detector when no event is detected. The backgroundconcentration is used as a baseline to determine the significance of anevent when there is a spike in methane readings. In the last step, theatmospheric stability is predicted from wind speed and direction toaccount for spreading of the plume.

Localization and Atmospheric Stability: The Gaussian plume model is thefoundation of the quantification algorithm and attributable to some ofthe major assumptions during modeling, e.g., multivariate normaldistribution of concentration and radial basis coordinate system. Theeffects of wind speed and direction, mixing, and atmospheric stabilityare accounted for in the Gaussian plume model.

With reference to FIG. 17 , a representation 1700 of a Gaussian plumemodel (adapted from J.M. Stockie (2011)) is illustrated. As shown in theFIG. 17 , a plume (for example, of methane gas) is modeled as radiallyextending with horizontal and vertical spreading. For an emission rate Qg/s and wind velocity of u m/s, the concentration distribution profileis known as the Gaussian plume solution for some sensor height of zmeters and source height of H meters, as provided in the belowequations:

$\begin{matrix}{{C\left( {r,y,\ z} \right)} = {\frac{Q}{4\pi{ur}}\exp\left( {- \frac{y^{2}}{4r}} \right)\left( {{\exp\left( {- \frac{\left( {z - H} \right)^{2}}{4r}} \right)} + {\exp\left( {- \frac{\left( {z + H} \right)^{2}}{4r}} \right)}} \right)}} & (2.1)\end{matrix}$ $\begin{matrix}{r = {\frac{1}{2}{\sigma^{2}(x)}}} & (2.2)\end{matrix}$ $\begin{matrix}{{\sigma^{2}(x)} = {ax^{b}}} & (2.3)\end{matrix}$ $\begin{matrix}{\begin{matrix}{{x = {R\cos\left( {\theta - \theta_{0}} \right)}}\ ,} & {y = {R{\sin\left( {\theta - \theta_{0}} \right)}}}\end{matrix}\ } & (2.4)\end{matrix}$

In the equation (2.1), the first term Q/4πur is the initial condition orinitial flux; and the second term exp (−y²/4r) is the spreading of theplume off the y-axis. The third and fourth termsexp−(z−H)²+exp−(z+H)²/4r4r are the change in the plume as a function ofheight. The parameter a is the standard deviation of the concentrationdistribution and r represents its variability; y, z are the Cartesiancoordinates; a, b are the diffusion parameters related to theatmospheric stability class. Depending on the hour of the day, arelationship between the time of day, Pasquill-Gifford stability class,and the diffusion parameters can be determined. In the equation (2.1),the concentration distribution profile is projected to radial basiscoordinates.

A function T dependent on wind direction may be defined using equationbelow:

$\begin{matrix}{{T_{1} = \frac{1}{2\pi{u\left( {aR^{b}} \right)}^{2}}},} & (2.5)\end{matrix}$ $\begin{matrix}{{T_{2} = {\exp\left( {- \frac{R^{2}{\sin^{2}\left( \frac{\pi\left( {\theta - \theta_{0}} \right)}{180} \right)}}{2\left( {aR^{b}} \right)^{2}}} \right)}},} & (2.6)\end{matrix}$ $\begin{matrix}{{T_{3} = {\exp\left( {- \frac{\left( {z - H} \right)^{2}}{2\left( {aR^{b}} \right)^{2}}} \right)}},} & (2.7)\end{matrix}$ $\begin{matrix}{T_{4} = {\exp\left( {- \frac{\left( {z + H} \right)^{2}}{2\left( {aR^{b}} \right)^{2}}} \right)}} & (2.8)\end{matrix}$

During localization, there is a probability pn,m that a detector n=1, 2,. . . , N can “see” a source m=1, 2, . . . M at a given time as afunction of wind speed and direction. The angle θ₀ and radial distance Rbetween the source and detector is first measured and then the flux fromsource m is computed using concentration data from detector n. FIG. 18is a graphical representation 1800 illustrating radial distance andangle between source S1 and detector Dl. The conditional probability isthen given by (2.9)P(S _(m) |D _(n) ,t _(k))=pn,m,n=1,2, . . . ,N,m=1,2, . . . M,k=1,2, . .. J,  (2.9)

The probability P(Sm|Dn,tk) in (2.9) is the probability source m emitsgiven readings from detector n. Essentially, it is the probability ofseeing a leak at the source. The probability curves are given for allpossible paths of the Gaussian plume in radial coordinates. The inputparameter θ₀ ^(n,m) is the angle between the specific source m anddetector n. The function T is dependent on wind direction, such that

$\begin{matrix}{{{T\left( \theta_{j}^{n,m} \right)} = \frac{T_{1} \times {T_{2}\left( \theta_{j}^{n,m} \right)} \times \left( {T_{3} + T_{4}} \right)}{\rho_{gas}}},{j = 1},2,\ldots,J,} & (2.1)\end{matrix}$ $\begin{matrix}{{\theta^{n,m} = \left( {{{{- 8}9} + \theta_{0}^{n,m}},\ {{89} + \theta_{0}^{n,m}}} \right)},{m = 1},{2\ldots},M,{n = 1},2,\ldots,N,} & (2.11)\end{matrix}$

In addition, the condition is set that if θ_(j) ^(n,m)>360, j=1, 2, . .. , J, then θ_(j) ^(n,m)>360−θ_(j) ^(n,m). The constant 6.56×10-4 is forthe conversion of units between parts per million volume and g/m³.

The next step is to normalize (2.10) at time t_(k), k=1, 2, . . . , Jgiven some wind direction θ_(k) ^(n,m) and wind speed u_(k). The sum ofprobabilities for the sources S_(m) and the residual probability orbackground B is 1, where,

$\begin{matrix}{{{P\left( {S_{m}{❘{D_{n},t_{k}}}} \right)} = \left( {{\overset{˜}{T}\left( \theta_{1}^{n,m} \right)},\ldots,{\overset{˜}{T}\left( \theta_{J}^{n,m,} \right)}} \right)},{{{at}{time}t_{k}{for}k} = 1},2,\ldots,{J;{m = 1}},{2\ldots},M,{n = 1},2,\ldots,N,} & (2.12)\end{matrix}$ $\begin{matrix}{{{P\left( {B{❘{D_{n},t_{k}}}} \right)} = {1 - {{\sum}_{m = 1}^{M}{P\left( {S_{m}{❘{D_{n},t_{k}}}} \right)}}}},} & (2.13)\end{matrix}$ $\begin{matrix}{{{\overset{\hat{}}{T}\left( \theta_{i}^{n,m} \right)} = \frac{T\left( \theta_{j}^{n.m} \right)}{{\sum}_{j = 1}^{J}{T\left( \theta_{j}^{n,m} \right)}}},{i = 1},2,\ldots,{J.}} & (2.14)\end{matrix}$

FIG. 19 shows a schematic representation 1900 an example of analysisperforming localization of a site 1902 (e.g., Colorado StateUniversity's METEC Lab experimental site) with the probability curves1904 given as a function of wind direction and graph 1906.

The associated functions for localization and atmospheric stability maybe the following: radial gaussian, flux, return BNL dispersioncoefficients, compute geometry, site probability.

The next phase of the quantification algorithm is to detect events fromeach set of concentration data corresponding to its respective detector.A preliminary analysis was developed to look at 3-minute intervals of1-minute data to see if there is a peak in concentration during thisperiod of time. The peak in concentration is analyzed by using thedifference formula to approximate the gradient or slope of theconcentration curve. If it exceeds a threshold of 0.75, then the timeperiod is classified as an “event” with a nonzero flux rate; otherwise,it is classified as “no event” with a negligible flux rate. The startand end time of the event must also be specified. The event is said tostart if the change in concentration is greater than some δt, and theevent ends when it is less than −δt. In this way, the event is assumedto be like a symmetric curve with about the same slope for the start andending of the event.

The baseline concentration must first be specified as a continuous line.To do so, the background concentration is calculated using the datacorresponding to wind direction between ±25 degrees from θ0. Outside ofthe events, the data is removed 15 minutes before and after an eventfrom the background concentration. Then, a continuous 5-minute rollingaverage is taken over designated background concentration. If there isno concentration data moving forward, the backward fill is applied topopulate missing values forward in time; and the forward fill is appliedto propagate the last observation forward. Then, the wind speed wasfiltered, so that it cannot drop below 0.5 m/s and exceed 10 m/s. FIGS.20A and 20B highlights an example of five events detected along with thebackground concentration.

With reference to FIGS. 20A and 20B, graphical representations 2000A,2000B of results from (a) event detection and (b) backgroundconcentration are depicted. The associated functions for event detectionand background calculation may be the following:quantify_and_detect_leaks, and quantify.

In some configurations, total hourly flow rate may be determined usingeither (i) maximum probability based method or the (ii) total weightedaverage method. For method (i) in (4.1), the total hourly flow rate isdisplayed as the average of hourly sensor based flow rate for the mostprobable source. This average is restricted to sensors with conditionalprobabilities higher than 75% or attributable to the sensor with thehighest probability reading sensor if no other sensors have aprobability reading higher than 75%. This method works best if only onesource is active and the rest are inactive with negligible or noemissions. The maximum and minimum flow rates at each sensor areprovided if it has a specific flow rate over 75%. For method (ii) in(4.2), the flow rate of each source is the weighted average as theaverage of all partial flow rates of the sensors weighted by the hourlyconditional probabilities for each sensor with probabilities higher than100/M (100 per million). The flow rate for all sources is then summed toform a total flow rate for sources that have a total probability of leakover 100/M. This method is more efficient at accounting for multiplesources but less so for a single emitting source.

$\begin{matrix}{{{{{Method}(i)}\overset{\sim}{Q_{m}}} = {Q_{m}\left( {P_{> {{0.7}5}}\left( {S_{m}{❘t_{60}}} \right)} \right)}},{m = 1},2,\ldots,M,} & (4.1)\end{matrix}$ $\begin{matrix}{{{{{Method}({ii})}:\overset{˜}{Q}} = {{\sum}_{m = 1}^{M}{P_{> \frac{100}{M}}\left( {S_{m}{❘t_{60}}} \right)}Q_{m}}},} & (4.2)\end{matrix}$ $\begin{matrix}{{P\left( {S_{m}{❘t_{60}}} \right)} = \frac{{\sum}_{n = 1}^{N}{C\left( {D_{n},T} \right)}{P\left( {S_{m}{❘{D_{n},T}}} \right)}}{{\sum}_{n = 1}^{N}{C\left( {D_{n},T} \right)}}} & (4.3)\end{matrix}$

In one alternative configuration, an air quality measurement system mayinclude computer-enabled instructions outlined as followed:

-   -   1. Connect to Amazon Web Services API using your designated        username and password    -   2. Connect to a Dashboard and pull site data for a given period        of time and site ID        -   (a) Detector locations and resampling for 1-minute data        -   (b) Equipment/source geometry    -   3. Compute the methane concentration curve for a source S in        radial coordinates centered at S over the vector of wind        directions θ∈(−89+θ0, 89+θ0)    -   4. Define the diffusion parameters a, b        -   (a) Create a dictionary with values of the diffusion            parameters a, b from the Gaussian plume model given the            Pasquill-Gifford (PG) atmospheric stability class        -   (b) Associate the PG class to the time of day and then go            into dictionary to select appropriate values of a, b    -   5. Create polygon geometry around sensors and detectors        -   (a) Create polygon objects in a polygon dictionary from the            equipment geometry        -   (b) Calculate the centroids and (backward and forward)            azimuth angles of the polygon equipment and sensors        -   (c) Then, calculate the distance between the centroids of            the polygon equipment and sensors        -   (d) Calculate the average background angle    -   6. Create the normalized probability curves that one detector        sees a source    -   7. Quantify and detect leaks by isolating plumes seen by        detectors in time series concentration data        -   (a) Isolate detector concentration and look at fixed            intervals to see if an event occurs during this period of            time        -   (b) Classify whether there is an “event”        -   (c) Find baseline concentration from background    -   8. Run all of the functions in the final function that calls        them all in quantify

The instructions provided immediately above for an illustrative airquality monitor may be configured to provide functions as follows:Import site data over a specified time period: pull sitedata(siteId,startDate,endDate)

Inputs:

-   -   siteId—identification number associated with site;    -   startDate, endDate—time frame over which to import site data        Outputs:    -   site df, i.e, data frame of detector and equipment locations,        detector time series data; and

Compute the methane probability curve for a source S in radialcoordinates centered at S over the wind directions θ∈(−89+θ₀, 89+θ₀)using Equation (2.1): radial gaussian (u,R,theta_θ,z,H,a,b)

Inputs:

-   -   u—wind speed in units of m/s    -   R—radial distance between detector and sensor in units of meters    -   Theta_0—angle between detector and sensor    -   z—height of detector(s) in units of meters    -   H—height of source(s) in units of meters    -   a,b—diffusion parameters dependent on atmospheric stability        Outputs:    -   returned vector, i.e., concentration distribution curve

Convert from concentration to flux: flux(cppm,background,windspeed,sigma y,z,H)

Inputs:

-   -   c ppm—concentration of methane in units of ppm        -   background—background methane concentration in units of ppm        -   windspeed—wind speed in units of m/s        -   sigma y—horizontal dispersion coefficient in m        -   z—height of detector(s) in units of meters        -   H—height of source(s) in units of meters            Outputs:    -   flux gs, i.e., methane flux rate in units of g/m³

Finding the appropriate values of the diffusion parameters a, b from theGaussian plume model given the time of day and Pasquill-Gifford (PG)atmospheric stability class: return_BNL_dispersion coefficients(hours)

Inputs:

-   -   hours—vector of hours over which data is being recorded and        read-in        Outputs:

a arr, b arr, i.e., vector of diffusion parameters over time period ofmeasured data Creating a dictionary with values of the diffusionparameters a, b given the PG class: BNL dict

Store polygon objects from the equipment geometry and sensors in apolygon dictionary; calculate the centroids of the polygon equipment andsensors, and then calculate the distance between the centroids of thepolygon equipment and sensors R and azimuth angle θ₀ (for backgroundtoo): compute geometry(equipment geometry,sensor locations)

Inputs:

-   -   equipment geometry—contains the location and information about        the sources/equipment    -   sensor locations—contains the locations and information about        the sensors        Outputs:    -   geom dict, e.g., R, θ₀

Compute the normalized probability curves that one detector sees asource: site probability(geom dict,detectors, sources)

Inputs:

-   -   geom dict—output from the function compute geometry; it is a        dictionary containing the azimuth angles, radial distance,        centroids, etc. of the sources and detectors    -   detectors—contains the locations and information about the        sensors    -   sources—contains the location and information about the        sources/equipment        Outputs:    -   probability df, i.e., probability concentration curve of seeing        an event or no event at detector        -   See the upper right-hand corner of FIG. 18 for an example            output.

Quantify and detect leaks by isolating plumes seen by detectors in timeseries concentration data:quantify_and_detect_leaks(detectors,sources,site df,geom dict,probability df)

Inputs:

-   -   detectors—contains the locations and information about the        sensors    -   sources—contains the location and information about the        sources/equipment    -   site df—output from pull site data; data frame of iterables:        methane, wind speed, and wind direction    -   geom dict—output from the function compute geometry; it is a        dictionary containing the azimuth angles, radial distance,        centroids, etc. of the sources and detectors        Outputs:    -   events metadata, i.e., whether there was an event; flux df,        i.e., data frame of probability of concentration and flux rate

Run all of the preceding codes in one function calledquantify(siteId,startDate,endDate)

Inputs:

-   -   siteId—identification number associated with site    -   startDate, endDate—time frame over which to import site data

METEC Round 2 Testing and Validation Findings and Results of MVP1Quantification Model: In a field-testing campaign in real worldenvironment at a site (for example, Methane Emissions TechnologyEvaluation Center (METEC) at Colorado State University), illustrativeresults from developing, testing, and implementing methods forquantification of methane emissions from oil and gas facilities usingsensor nodes and analytics platform are presented. This platformintegrates detector data, meteorological conditions, and cloud analyticsto detect and quantify methane emissions for remote locations. Thisfirst minimum viable product for quantification (MVP1) has, or will be,updated by subsequent tests.

An illustrative installation of the present disclosure performed threedays of around the clock live methane emissions tests including daytime,nighttime and in between to investigate the diurnal effect onquantification methods. The design of experiment included a total offorty four test conditions (experiments) where programmed methanereleases were introduced from actual natural gas site structureincluding gas processing units, well heads, and storage tank batteries.A total of eight sensor nodes forming a larger sensor network weredeployed at the fence line of the 200 ft×280 ft site with a detector tosource distance ranging from 69 to 230 ft. The duration of each test was60 minutes followed by 15 minutes of no methane release to establishbaseline for the next new test and so on. Each test was repeated threetimes to examine various quantification models for reproducibility ofconsistent results. Methane release rates ranged from low, 0.05 to high,0.84 g/s which is a wide range that represents average well pademissions.

As part of quantification MVP1, two models (Model N and Model S) forquantification were developed. These models were thoroughly investigatedto determine presence of any problem(s) and to employ the bestevaluation methods for additional development(s). The quantificationmethods have demonstrated that it can detect methane leaks in the rangeof 0.05 g/s up to 0.85 g/s with a total site emission prediction errorranging from −16% to 3% at an average wind speed ranging from 0.5 m/s to6 m/s at the site footprint and sensor configuration mentioned earlier.Total predicted site emission is the cumulative predicted emission ratesof each experiment over the total test period of three days. The Truetotal site emission (cumulative over the full 3-day test period) was50.22 kg of methane whereas the predicted values were 58.1 kg and 48.74kg of methane using Model S and Model N respectively. FIG. 21 shows agraphical representation 2100 of the cumulative predictive emissions forthe site (METEC site) over the course of three days as compared to trueemissions. Further, FIG. 21 shows the METEC R2 comparison of true totalsite emissions (curve 2102) with predicted total site emissions frommodel N (curve 2104) and model S (curve 2106) (Cumulative Period of 3Days—July 21st to Jul. 23, 2021)

Both models were able to predict reasonably well except for few caseswhere unfavorable wind transport of emissions occurred. Since thiscontinuous monitoring technology relies on wind to advect air bornemethane molecules to detector, unfavorable wind conditions couldoccasionally result in placing the detector upwind of a given emissionsource, creating a weak signal to noise inhibiting the detector fromreceiving the right information on emission source concentration. Thiswould impact plume dispersion model ability to predict accurately.Nevertheless, this scenario is not typical for real life deployment asmodels may be able to use the wealth of wind diversity over time, whichwas not the case in a limited three-day testing in METEC facility.

Both model N and model S are based on the Gaussian Plume Model (GPM)principles with few different assumptions including smart sampling,weighted average, bootstrapping, radial GPM, cartesian GPM, dispersioncoefficients based on Pasquill-Gifford, BNL.

For example, in the site 1902 shown in FIG. 19 , detectors and methanerelease system may be deployed. For example, eight sensor nodes (Itshould be noted that the term “sensor node” may have been usedinterchangeably with the term “sensor”, or the term “detector”, or theterm “air quality monitor” or the term “sensing system” in thisdisclosure) may be set up around the test site (200 ft×280 ft) with adetector to source distance ranging from 69 to 230 ft. This is toreasonably ensure the detectors will activate and capture the gas plumeregardless of wind direction, enabling the system to operateautonomously. Wind speed and direction may be measured using ultrasonicwind sensors installed in some of the sensor nodes. The testing mayinclude a release of a controlled methane volume located at three mainsources, wellhead, separator, and storage tanks at different releaserates ranging from low, 0.05 to high, 0.84 g/s which is a wide rangethat represents average well pad emissions. The site may have provisionsto accurately regulate and measure flow rate using orifice meters,solenoid, and PLC.

FIG. 22 illustrates a workflow diagram 2200 depicting a framework ofquantification. As shown in, the quantification workflow diagram of FIG.22 , as field testing progresses, time series data from individualdetectors are streamed to Amazon Web Servers (AWS) in real time. Thedata is comprised of signals from the sensing element as it responds tolocal methane concentrations, at the location of the detector inaddition to wind speed in m/s and wind direction measurements (0o to360o). Detector data are pushed to AWS for pre-processing before beingpassed on to the developed model for emission rate and source locationprediction. When the data is downloaded into local servers, it is passedon to an extraction, transformation, and loading (ETL) computationalpipeline before being ready for the prediction algorithm. Theconcentration data (ppm) is augmented by GPS coordinates of theindividual sensors and a single file encompassing the experimental timeof a given test (typically one hour) before being ingested by the model.

Detector placement is initially decided prior to the testing campaign bystudying multiple wind rose diagrams from historical weather stationsdata and identifying the most likely dominant wind directions around thelocation of the testing. Visualization of time series and hourlyaggregated statistics of concentration, wind speed, and wind directionfrom all detectors and weather sensors enable the user to assess nodeengagement and to adjust the experimental setup, if necessary, tomaximize alignment of sensors with the dominant methane dispersiondirections by the prevailing wind as shown in FIG. 23 . FIG. 23illustrates a graphical representation 2300 of configuration of adashboard showing time-series concentration. FIG. 24 illustrates anexample wind rose diagram 2400 defined by a weather data for a site(e.g. the METEC site). As will be appreciated, wind rose diagrams aregraphical charts that characterize the speed and direction of winds at alocation. FIG. 25 illustrates another wind rose diagram 2500 including apredominate wind direction (shown as “1”), a secondary wind direction(shown as “2”), and a tertiary wind direction (shown as “3”) over aperiod of time (e.g. a year). FIGS. 26A-26G illustrates a wind rosediagram for each of a week, respectively including a predominate winddirection (shown as “1”) during that day. In other words, a wind rosediagram 2600A for Sunday (Aug. 1, 2021), a wind rose diagram 2600B forMonday (Aug. 2, 2021), a wind rose diagram 2600C for Tuesday (Aug. 3,2021), a wind rose diagram 2600D for Wednesday (Aug. 4, 2021), a windrose diagram 2600E for Thursday (Aug. 5, 2021), a wind rose diagram2600F for Friday (Aug. 6, 2021), and a wind rose diagram 2600G forSaturday (Aug. 7, 2021).

Data Pre-Processing: Signal-to-Noise Ratio (SNR): Data pre-processingmay be performed on data obtained by detectors running the atmosphericdispersion model. The goal of data pre-processing is to filter out anynoisy data before running the atmospheric dispersion model. One datapre-processing method may include analyzing Signal-to-Noise Ratio (SNR).

The idea behind SNR is to see if there is a significant peak in theconcentration profile that would indicate that the detector is pickingup an unknown or a known leak. The formula for SNR is relatively simplesince it involves taking the difference between the highestconcentration (x_(peak)) and lowest concentration (x_(base)) and scalingit by

$\begin{matrix}{\frac{1}{\sqrt{x_{peak}}}.} \\{{SNR} = \frac{x_{peak} - x_{base}}{\sqrt{x_{peak}}}}\end{matrix}$

The SNR is performed over each hour, e.g., 8:00 AM-8:59 AM, 9:00-9:59AM, etc., for each detector. If the SNR ratio is less than 0.7 from 8:00AM-8:59 AM, the data from this detector is turned off during that hour.Otherwise, the concentration data from the detector is deemed useful tothe model, and it is used in the flux calculations of the GPM. Followingthe example in FIG. 5 b , only two of the four detectors are retainedwhile Detectors W and SE are turned off since they have SNR<0.7. In theGPM, the concentration data from Detectors NE and N are the only onesused in the flux calculations. FIG. 27 shows a graphical representation2700 of an example of SNR associated with different detectors beforeeliminating two detectors W, SE with SNR<0.7. FIG. 28 shows a graphicalrepresentation 2800 of an example of SNR after eliminating the twosensors W, SE.

Wind Directional Filtering: Another method of data pre-processing may bewind directional filtering which may be performed in addition to SNR.Wind filtering is the process of turning off detectors that are outsideof the wind direction and focusing on detectors downstream of theemitting source. When detectors are within direction of the wind, theywill pick up an emitting source that is carrying a methane plumedownstream. If the detectors are not in the appropriate position of thewind with moderate dispersion, then these detectors should be able topick up the methane plume. Hence, they are turned off. The windfiltering algorithm will turn off detectors that fall out of bounds ofthe wind direction with the stability class to account for spreading ordispersion of the methane plume from its centerline. FIG. 29 is agraphical representation of an example of wind directional filteringprocess. As illustrated in the FIG. 29 , detectors (i.e., detectors W,SE) which are not in the appropriate position of the wind with moderatedispersion are turned off (i.e., filtered out).

In alternate embodiments, after transmitting the actual emissionsmeasurement to the cloud server, a population of the actual emissionsmeasurement may be filtered, for example, to identify cyclicalemissions. The cyclical emissions may be the periodic emissions from thesite that may take place as part of the operations at the site, andtherefore may not amount to inadvertent leakage. As such, the scope ofthe disclosure may be restricted to identifying the inadvertentemissions occurring due to leakage, or an accident. It may be noted thatthe cyclical emissions may be dependent on at least one of thefollowing: a time of day, a day of month, a month of year, temperature,and wind direction.

Plume Dispersion Model for Quantification of Methane Emissions: In areal environment, an industrial plume may propagate and diffuse from themoment an emission is released from a point source as shown in FIG. 17 .This transport process is the combination of diffusion (due to turbulenteddy motion) and advection (due to the wind) that defines the term,dispersion (Stockie 2011). The concentration of a contaminant releasewill be transported through the air in an axisymmetric pattern(idealized case). A method used in modeling this phenomenon may bederived from the advection-diffusion equation and results in decayingGaussian distribution profiles with distance. A dispersion model isessentially a computational procedure for predicting concentrationsdownwind of a pollutant source, based on knowledge of the emissionscharacteristics (stack exit velocity, plume temperature, stack diameter,etc.), terrain (surface roughness, local topography, nearby buildings),and state of the atmosphere (wind speed, stability, mixing height, etc.)(MacDonald 2003).

The complexity of the plume source inversion arises from the need torecover information about the source emission rate(s) and location usingconcentration signatures from a few detectors. These emissions arerelated through a highly nonlinear and high-dimensional turbulentdynamic that pervades the near surface atmosphere. A number ofanalytical and approximate solutions for atmospheric dispersion may bederived under a wide range of assumptions, boundary conditions, andparameter dependencies. One of these solutions is the Gaussian plumesolution, which is an approximate solution for single point-sourceemissions:

${C\left( {x,y,\ z} \right)} = {\frac{Q}{2\pi U\sigma_{y}\sigma_{z}}*{\exp\left( {- \frac{y^{2}}{2\sigma_{y}^{2}}} \right)}*\left\lbrack {{\exp\left( {- \frac{\left( {z - H} \right)^{2}}{2\sigma_{z}^{2}}} \right)} + {\exp\left( {- \frac{\left( {z + H} \right)^{2}}{2\sigma_{z}^{2}}} \right)}} \right\rbrack}$Where:

-   -   σy=S.D. of horizontal distribution of plume concentration=axb        (m)    -   σz=S.D. of vertical distribution of plume concentration=cxd (m)    -   C=Concentration at the detector (kg/m3)    -   H=Effective height of emission source (m)    -   U=Wind speed along x-axis, assuming invariable with height (m/s)    -   Z=Detector height above ground (m)

It should be mentioned that as part of this MVP1 two Models (Model N andModel S) based on the GPM principles with few different assumptions.Both the models may be converged to a common algorithm.

Data Post-Processing: The plume model outputs may include predictedrelease rates (or instantaneous fluxes) at each detector. The predictedrelease rates from each detector may be grouped together to form a bigsample of flux data called the population. After obtaining a fulltimeseries flux for each detector, bootstrap resampling may be performedto quantify the random errors and provide a confidence range for thestatistics reported. The mean flux for each detector may be calculatedand added to the population. Further, summary statistics and estimatedthe precision of the reported statistics may be reported using bootstrapresampling described immediately below.

As it will be appreciated by those skilled in the art, bootstrapping isa statistical procedure involving the generation of random samples withreplacement allowing us to quantify the random sampling errors andprovide a confidence interval along with all statistics reported.Confidence intervals are estimated computationally.

For example, in a collection of pennies, dimes, and quarters in one bagmay be called ‘the population’ A form of sampling called bootstrappingmay be utilized to collect samples from this population to generatebasic statistics (e.g., mean, standard deviation, confidence intervals,etc.). For example, an unbiased and a random sample of 5 coins may becollected from the bag of 50 coins. This process is repeated multipletimes to determine how much money someone is likely to pull out of thebag with 5 coins on average. There might be a case when someone pullsout $1.25 from 5 quarters and another case when someone pulls out $0.05from 5 pennies. Extreme cases are identified by repeatedly sampling 5coins, say 50 times. Sometimes, high, or low amounts are sampled fromthe bag (e.g., $1.25, and $0.05). They will be accounted for in theconfidence intervals as the lower and upper bounds of money collected.The confidence intervals provide us with a form of error statistics,which will be used to assess the variability in the population'sdistribution.

As will be understood, a confidence interval of [0.05 g/s, 0.10 g/s] forflux is much narrower compared to a confidence interval of [0.05 g/s,0.35 g/s]. When the confidence interval is narrower, the actual leakrate most likely falls between 0.05-0.10 g/s with less variability inwhat its actual value. However, when the confidence interval for flux is[0.05 g/s, 0.35 g/s], there is more variability and uncertainty in theactual leak rate. It can fall anywhere between 0.05-0.35 g/s andpossibly even outside of these bounds. These confidence intervals,hence, provide us with uncertainty. The formula for the confidenceinterval is given as follows:

${{95\%{CI}} = {\mu \pm {{1.9}6\frac{\sigma}{\sqrt{n}}}}},$where μ is the mean flux or release rate, σ is the standard deviation ofthe population of fluxes, and n is the total number of samples in thepopulation.

Therefore, bootstrapping may be used for summarizing the predictedrelease rates from each detector and for determining an average releaserate that is an unbiased representation of the true leak rate. As such,after transmitting the actual emissions measurement to the cloud server,bootstrapping of the plurality of actual emissions measurement (obtainedby the plurality of air quality monitors) may be performed. The averagerelease rate does not depend on any detector with bootstrapping. If onedetector overestimates the true leak rate while another detectorunderestimates the true leak rate, bootstrapping will average over thesetwo cases. The key characteristic of bootstrapping is utilized becausethe following are unknown: (1) which detector is closest to the source;and (2) which detector is closely estimating the actual leak rate.

As mentioned earlier, as part of quantification MVP1, two models (ModelN and Model S) may be developed for quantification, investigate theproblem, and employ the best evaluation method for next development,MVP2. FIGS. 30 and 31 summarize the preliminary performance of thepredictive algorithm for the forty four experiments conducted at METECtest site (Cumulative Period of 3 Days—July 21st to July 23rd/2021). Inparticular, FIG. 30 is a graphical representation 3000 of fluxpredictions for all emission sources based on predictive algorithm forthe experiments conducted at a site (e.g., METEC test site) over acumulative period of three days. FIG. 31 is a graphical representation3100 of flux prediction error for all emission sources based onpredictive algorithm for the experiments conducted at the site over thecumulative period of three days. Both model predictions may correlatewell with the true emission rate as indicated by the proximity of thepredictions to the forty-five-degree line.

As such, the quantification methods may be able to detect methane leaksin the range of g/s up to 0.85 g/s with a total site emission predictionerror ranging from −16% to 3% at an average wind speed ranging from 0.5m/s to 6 m/s at the site footprint and sensor configuration mentionedearlier. Total predicted site emission is the cumulative predictedemission rates of each experiment over the total test period of threedays. For example, it was observed that the true total site emission was50.22 kg of methane whereas the predicted values were 58.1 kg of methaneusing Model S, and 48.74 kg of methane using Model N. Referring onceagain to FIG. 21 , the graphical representation 2100 shows thecumulative predictive emissions for METEC Site Emissions over the courseof three days as compared to true emissions.

Additional testing planned on September at METEC may allow for furtherestimation of the error distribution as well as the prediction intervalwidth and the overall emission rate prediction trend. As mentionedabove, FIG. 30 illustrates flux predictions for all emission sources,and FIG. 31 illustrates flux prediction error for all emission sources.A comparison of true flux predictions (total site emissions) and thepredicted flux predictions—with reference to model N and model-S isshown in FIG. 21 .

A breakdown of total site emissions quantification methods per sourceagainst the true emitted quantities of methane may be recorded. It maybe noted that both models (model N and model S) may be able to predictreasonably well except for few cases where unfavorable wind transport ofemissions occur. Since this continuous monitoring technology relies onwind to advect air borne methane molecules to detector, unfavorable windconditions could occasionally result in placing the detector upwind of agiven emission source, creating a weak signal to noise inhibiting thedetector from receiving the right information on emission sourceconcentration. This would impact plume dispersion model ability topredict accurately.

FIG. 32 illustrates a schematic representation 3200 of time-seriesconcentration and wind speed for an example experiment study. A windrose diagram 3202 shows that the prevailing wind during this experimentwas from WNW to ESE. Further, SNR graphs 3204 (i.e., graphs 3204A,3204B, 3204C, 3204D, 3204E, 3204F, 3204G, 3204H) corresponding to eightsensors are shown along with determined SNR values. As a result ofanalysis of the SNR, resulting in seven out of eight sensors becomingupwind during this experiment which lasted for one hour. The SNR valuesconfirmed this finding are indicated in the corresponding graphs 3024for each of the sensors. As shown, SNR<0.7 (i.e., graphs 3204A, 3204C,3204D, 3204E, 3204F, 3204G, 3204H) where detectors were not pickingstrong methane signal, and SNR>0.7 (3204B) where the detector isdirectly engaged in measuring the methane concentration.

In one configuration, in the current experiment study, only one methanedetector is in the downwind measuring concentration. The quantificationmodel is able to predict better excels with large amount of qualitydata.

To evaluate the wind impact on model prediction error, a matrix ofdifferent parameters may be created, including number of active sensorsdownwind, number of inactive sensors upwind, prevailing wind direction,and average wind speed for each of the forty four experiments (hourlybasis, 56 hours), as shown in FIG. 33 . It may be noted that the numberof detectors in the downwind may be inversely proportional to theprediction error due to feeding the model with more useful informationthat allows for better predictions as explained earlier. However, sincethis is a multivariant problem, therefore, the number of detectors maynot be the only determinant as wind direction and speed have some roleto play as well. During real deployment in oil field whererepresentative detectors are permanently installed for continuousmonitoring the above scenario of unfavorable wind will not be likely aswind direction and speed likely change during the day. FIG. 33illustrates a graphical representation 3300 of flux prediction error (%)as a Function of wind speed (WS), wind direction (WD), upwind detectors,downwind detectors during the 51 hour test period at the site (i.e.,METEC site).

The experimental errors can be divided into three categories, namelymeasurement error, bias error, and random sampling error. Themeasurement error is the inherent error involved in using a specificsensor that relies in its operation on some physical effect representingthe response of the sensor to the quantity being measured. This error isoften sizable in magnitude and could be quantified using calibrationagainst a known reference. For sensor nodes experiments were conductedto estimate concentration errors at various reference concentrations.The bias error results from sampling specific regions of the underlyingprobability distribution, thus favoring certain times or operatingconditions for the components under investigation. The only way toensure sampling all potential values for emission concentrations may beto measure emissions for long enough to ensure quasi-stationaryprobability density functions (pdf) of the emission from a givenequipment. Some models estimated that in most cases the equivalent ofthree-hour worth of data (i.e., every minute) is reasonable to ensurequasi-stationary pdf. This is not a general rule of thumb, it is justtrue for a representative dataset obtained from METEC site data. Therandom sampling error may occur when measuring a sample of a givenquantity rather than the full population. In different realizations ofthe samples estimates may be different. In practice this is typicallythe smallest of all above three types of errors. It can be easilyquantified using the bootstrapping techniques described above.

The performance of model S with data pre-processing and post-processingmay be tested against a second round of experiments (forty fourexperiments) at the METEC site for three days (for example, from July21st to Jul. 23, 2021). The results from the model S may be comparedagainst the Gaussian Plume Model (GPM), which is essentially the model Swithout SNR and bootstrapping. The predicted release rates from the GPMmay be the average flux from all detectors. Each experiment may berepeated three times, i.e., there are fifteen sets of three similarexperiments.

Model S and GPM may be run over the fifteen sets of experiments to testthe efficacy of data pre-processing and post-processing. A 95%confidence interval in the predicted release rates for GPM and Model Sare shown in the y-axis of FIG. 34A-34B, where the center is thepredicted (or average) release rate. Uncertainty in the data or trueflux rate itself is then shown along the x-axis. There is an improvementin the predicting release rate, which can be seen as the short verticalerror bounds of Model S.

In some configurations, error bounds of Model N may be added forenabling comparison against GPM and Model S and highlight thedifferences. FIG. 34A illustrates a graphical plotting 3400A of errorbounds in the GPM, and FIG. 34B illustrates a graphical plotting 3400Bof error bounds in the Model S.

The above sensing and analytics platform (which was tested in areal-world environment at METEC site of Colorado State University as apractical test site during the said period) integrates detector data andcloud analytics to offer a complete IoT solution for remote locationswhere power availability and communications to the cloud may bechallenging.

As part of quantification roadmap, the quantification methods are ableto detect methane leaks in the range of 0.05 g/s up to 0.85 g/s with atotal site emission prediction error ranging from −16% to 3% at anaverage wind speed ranging from 0.5 m/s to 6 m/s at the 200 ft×280 ftsite and a detector to source distance ranging from 69 to 230 ft. Totalpredicted site emission is the cumulative predicted emission rates ofeach experiment over the total test period (of three days).

It may be noted that wind speed and direction variability as well astest duration and sensor placement may lead to some variability amongstreplicates for the same flow rate.

As already explained in conjunction with FIGS. 11A-11E, the plume fluxof the plume of emissions at the site may be determined by receiving apredetermined number of samples of the plume at a plurality of angles ofthe plume by the plurality of air quality monitors (i.e., sensorssystems 1120) installed at the site. Further, an associatedconcentration point may be registered based on the plurality of angles.A fit of a point cloud may be obtained. When the measurements occur inidealized conditions of the site parameters, the plume flux may becalculated using a mass conservation equation by multiplying an areaconcentration of the plume cross section by its normal speed and byestimating the plume concentration in the height direction. The siteparameters may include wind speed, wind direction, temperature, andother parameters associated with the site.

Referring now to FIG. 35 , a plan 3500 of an example site undermonitoring is illustrated. The site may include equipment 3502, and aplurality of air quality monitors 3504 (as mentioned before, air qualitymonitors may also be referred to as sensors, sensors systems, sensingsystem, detectors in this disclosure). Further, a method of installingan air quality monitor system at the site may be performed. The methodmay include surveying the site by procuring: an equipment log of aplurality of leak-prone equipment 3502 at the site, a centroid 3506 ofthe leak-prone equipment, and a wind-rose diagram representative of windat the site. Surveying the site may further include procuring a 3D pointcloud of topography of the site and procuring a 3D point cloud of theleak-prone equipment 3502 of the site. Upon surveying, the wind-rosediagram may attached be to the site.

The wind rose diagram is already illustrated in the FIG. 25 . As shownin the FIG. 25 , the wind-rose diagram may include the predominatedownwind direction (“1”), the secondary downwind direction (“2”)angularly offset from the predominate downwind direction (“1”), and atertiary downwind direction (“3”) angularly offset from the predominatedownwind direction (“1”) and oppositely disposed from the secondarydownwind direction (“2”).

A predominate air quality monitor 3504(1) may be installed at the sitein the predominate downwind direction (“1”) from the centroid 3506 at alocation where the predominate air quality monitor 3504(1) has a maximalangular separation between the leak-prone equipment 3502. Beforeinstalling the predominate air quality monitor 3504(1), a site operatormay be instructed to install a first vertical object (for example, apost, a pole, or any vertically aligned shaft) where the predominate airquality monitor 3504(1) will be installed. The predominate air qualitymonitor 3504(1) may be installed to the first vertical object. Further,a secondary air quality monitor 3504(2) may be installed in thesecondary downwind direction (“2”) from the centroid 3506 where thesecondary air quality monitor 3504(2) has minimal observational overlapwith the predominate air quality monitor 3504(1). Before installing thesecondary air quality monitor 3504(2), the site operator may beinstructed to install a second vertical object where the secondary airquality monitor 3504(2) will be installed. The secondary air qualitymonitor 3504(2) may be attached to the second vertical object.Furthermore, a tertiary air quality monitor 3504(3) may be installed inthe tertiary downwind direction (“3”) from the centroid 3506 where thetertiary air quality monitor 3504(3) has minimal observational overlapwith the predominate air quality monitor 3504(1) and with the secondaryair quality monitor 3504(2). Before installing the tertiary air qualitymonitor 3504(3), the site operator may be instructed to install a thirdvertical object where the tertiary air quality monitor 3504(3) will beinstalled. The tertiary air quality monitor 3504(3) may be attached tothe third vertical object.

The predominate air quality monitor 3504(1), the secondary air qualitymonitor 3504(2), and the tertiary air quality monitor 3504(3) may beconfigured to obtain the first weather reading of local weather from aweather station and modify transmission of an emission data according tothe weather reading obtained from the weather station. This is alreadyexplained in conjunction with FIG. 7A. As mentioned before, the sensorsystem (or, the air quality monitor) may include a weather sensor system711 (also referred to as weather station 711). The weather sensor system711 may include sensing elements to measure wind speed and direction.The wind speed and direction may be measured by a combination of a windvane and an anemometer, or by an anemometer alone such as in the case ofusing an ultrasonic anemometer.

Further, a predominate connector may be communicatively coupled to thepredominate air quality monitor 3504(1). Further, a predominate weatherstation may be communicatively coupled to the predominate air qualitymonitor 3504(1) at the predominate connector. Similarly, a secondaryconnector may be communicatively coupled to the secondary air qualitymonitor 3504(2). Further, a secondary weather station may becommunicatively coupled to the secondary air quality monitor 3504(2) atthe secondary connector. In the same way, a tertiary connector may becommunicatively coupled to the tertiary air quality monitor 3504(3), anda tertiary weather station may be communicatively coupled to thetertiary air quality monitor 3504(3) at the tertiary connector.

The weather data from each of the predominate weather station, thesecondary weather station, and the tertiary weather station may betransmitted to a cloud computing device (for example, “Amazon WebServices” or simply “AWS”). This weather data may be analyzed todetermine redundant or non-contributing weather data. Further, at leastone of the predominate weather station, the secondary weather station,and tertiary weather station may be removed. As will be appreciated, allthe weather stations (i.e., the predominate weather station, thesecondary weather station, and the tertiary weather station) may notcontribute to the analysis, and therefore, the non-contributing weatherstation may be discarded.

In some embodiments, a ground temperature probe may be communicativelycoupled to at least one of the predominate air quality monitor 3504(1),the secondary air quality monitor 3504(2), and the tertiary air qualitymonitor 3504(3). This ground temperature probe may provide a groundtemperature. One of the predominate air quality monitor 3504(1), thesecondary air quality monitor 3504(2), and the tertiary air qualitymonitor 3504(3) may transmit the ground temperature (for example to the“AWS”). Based on the ground temperature, a diffusion-area of emissionsmay be estimated. It may be noted the ground temperature, or thediffusion-area of emissions may be fed to the plume model for analysis.

In an illustrative configuration of the system, the environmentalsensors are not collocated to the target gas sampling point. Asexplained in this disclosure, the collocation of environmental sensorssuch as the anemometer with the gas analysis sensor intake may improvethe interpretation of the data because of the effect of topology andobstacles on the transport of the target gas. To this end, as mentionedabove, the weather station may include the anemometer which may furtherinclude a due-North indicia. The weather station may be communicativelycoupled to the predominate air quality monitor 3504(1) at the connector.The due-North indicia of the anemometer may be aligned to North of theEarth. A first weather reading of local weather may be transmitted fromthe weather station. The weather reading of local weather may include awind speed and a wind direction.

In some embodiments, multiple sensors, for example three sensors (i.e.,the predominate air quality monitor 3504(1), the secondary air qualitymonitor 3504(2), and the tertiary air quality monitor 3504(3)) may bedeployed at the site, for example a gas pad. If the topology andobstacles configuration allows the environmental variables such as winddirection and wind speed are only marginally variable from theperspective of the different sensors deployed at the site. It may thenbe possible to reduce the number of environmental sensors, such asanemometers, by only positioning a single environmental sensor formultiple gas sensors. For example, on the site with three gas sensors,only one anemometer may be collocated with one of the three gas sensors,while no anemometer is used with the remaining two gas sensors. Thisallows for a reduction of the cost of deployment with only a marginalreduction of the efficacy of localizing, quantifying or qualifyingemissions.

One illustrative configuration of the disclosure concerns the deploymentof sensor to a site and the collection of site metadata. As mentionedabove, once a site is selected for continuous monitoring, informationabout the site (i.e., surveying) is first collected in order to identifythe best deployment locations. First site boundary and topologies areobtained. This may be offered by the site owner, or by consulting asatellite map databank. For example, in the case of a natural gas pad,the edge of the 50×75 m pad may be identified, and the terrain may beobtained using lidar maps from google earth. Then, the emplacement ofequipment groups that are to be observed are identified. This may bedone by inspection of the site, LIDAR mapping or by satellite imageanalysis. The equipment groups type, geometries and location arecollected to establish the geometry and location of sources in thepredictive simulations. For example, a trained operator may identify theequipment group and their size from satellite image and add then to thesite topology of the digital twin. Additional local topology informationabout the terrain surrounding the site may also be added to the digitaltwin to improve simulations, for example by adding obstacles like treesand buildings, following a process similar to the identification ofequipment groups. The next step or concurrent step is to identify localweather patterns. Historical wind conditions of the site may beextrapolated from the wind conditions at a proximate weather station, inparticular the identification of the primary and secondary winddirections. For example, the cli-MATE tool from the Midwestern RegionalClimate Center database may be used to construct historical wind rosefrom reference weather stations in the proximity of the site. Once thehistorical weather data is obtained, the position of the sensors may bedecided. The sensors may follow deployment rules that are sitedependent. In general, the objective is to maximize separateobservations of the observed equipment groups or areas of interest. Thismeans that the angular separation of the centroid of each equipmentgroup from the perspective of the sensor should be maximized to enhanceplume differentiation. Second, the sensor may only be deployed in anallowed area of the site. In the case of oil and gas pads, the siteboundary is generally allowable as it is part of the site and far enoughaway from the hazard zone around the equipment groups. Third, theposition of the sensor shall maximize the number of plume observations,this means that sensor shall be placed downwind of the observedequipment groups. With a limited number of sensors, this means thatsensors shall be placed with regard to the principal (i.e., predominate)and secondary wind directions extracted from the historical weatherdata. If additional sensors are allowed, these shall be placed tomaximize angular coverage of the equipment groups.

The first sensor (or predominate air quality monitor) is thereforeplaced close to the downwind direction of the principal (or,predominate) historical wind direction from the centroid of theequipment groups in a position that maximizes angular separation of theequipment groups. Assuming a secondary wind direction exists, a secondsensor (or secondary air quality monitor) shall be placed downwind ofthe secondary historical wind direction in front the centroid of theequipment groups in a position that maximizes separation of theequipment groups and minimizes observational overlap with the firstsensor. Subsequent sensors shall follow equivalent rules if additionalsecondary wind direction exists or maximize angular coverage of thesite. For example, in a three sensor deployment on an oil and gas site,the first sensor position may be selected north of the site because ofthe south principal wind direction. The second sensor may be positionedsouthwest because of the secondary northeast wind direction and thethird sensor (or tertiary air quality monitor) location may be set eastof the site to maximize coverage. The exact position of the sensor maybe shifted by few degrees based on local conditions and angularcoverage. In the precedent example. The third sensor location may beshifted to southeast because this would give it a better angularposition for observing all the equipment groups.

Once the prospective sensor position is established, the map ofpotential sensor location is shared with the operator of the site forapproval and for site preparation. The operator may move or object tocertain locations due to risk, need of access or future developmentproject. The position may then either be corrected to accommodate thisor the alternate location provided by the operator accepted. Theoperator may then proceed to the site preparation. For anchored sensors,this means the position of an anchor (e.g., a T-post) for the fasteningof the sensor. Once the site preparation are over, the sensor systemsmay be deployed at the specified location of the site.

Optionally, the position of the sensor may further be shifted. This mayhappen if the operators plan require the sensor to be removed (e.g., thesite may be modified) or if the observation data from the continuousmonitoring of the site is suboptimal (e.g., the historical wind datafrom a proximate weather station was not applicable to the site). A newplan from the data acquired by the deployed sensors may then beconceived to relocate the sensor to more favorable locations.

As explained in conjunction with FIGS. 12 and 13 , methods for thereduction of real time simulation cost by simulating many representativeconditions in advance and using inverse methods for identifying matchingconditions and predicting flux and source localization are provided. Onealternative embodiment is the simulation in real time of the transportproblem using real time experimental data, such as weather conditions,stability class and so on. While this may result in additionalcomputational cost, this would reduce some of the modeling error byhaving more accurate specification of the boundary conditions. In someinstances where dynamics is of importance, i.e., when the windconditions are shifting during transport, this may yield more accurateresults. It should be noted that for direct real time simulations, onemay use a fully resolved advection diffusion transport model withappropriate closure of the turbulence flow (i.e., LES, RAS, etc) or usea reduced order model such as the gaussian plume model.

An optional computational cost saving approach may be adopted tosub-select periods of interest to simulate, rather than simulate theentire time sequence. For example, in a 24 hour period, only smallerperiods within that 24 h period may be simulated, rather than the entireperiod, say 1 hour. The selection of the appropriate period may bederived directly from the data. For example, emissions may only bedetected by the sensor systems when the wind direction is appropriate.In that case, only when the wind direction is within a certain rangewould the simulation be run. Another discriminant may be theconcentration intensity detected by the sensor systems. In this case,only when an outlying concentration enhancement is detected that thesimulation be run. An outlying concentration even would be indicative ofan emission plume being detected.

An additional computational cost saving approach may lay in the choiceof model. A reduced order model, such as the Gaussian Plume Model mayfirst be run in real time for all selected periods of interest. This mayallow for a first pass at quantification, localization, andqualification of emission. Then in a second step, a fully resolvedadvection diffusion transport model may be run to confirm or reduce theuncertainty of quantification, localization, and quantification onselected time periods of interest where the uncertainty of the reducedorder model is higher than the uncertainty of the fully resolved model.

Referring back once again to FIG. 13B, the flowchart related to theexecution of a real time model is illustrated. Fundamentally, the methodin FIG. 13B is similar to the one given in FIG. 12 and FIG. 13A. Inparticular, the intermediary result of a relational matrix relatingemission flux to emission concentration is shared by all method, whichthen requires an inverse method to solve. The initial problem to besimulated is slightly different, however. While the digital twin modeland parameters are similar, and the search space for emission sourcesand flux amount is the same, the simulation uses weather measurementsfor the generation of initial and boundary conditions. In particular,temperature, pressure, hygrometry, and wind information may be used.Derived variables such as stability class, updraft, turbulent energy,standard deviation of wind and other input parameters may also bemeasured and calculated. In particular, the measurement of winddirection and speed at different points of the digital twin may be usedto generate boundary conditions that mimic the boundary conditions ofthe real site during a period of interest. A direct simulation may thenbe conducted to identify the transport of the target compound byassuming variable sources and flux rates (together emission variables)[Ej]. This can be done using multiple simulations using differentemission variables, or by simulating the transport from multiple sourcesand or multiple target gas in a single simulation over the period ofinterest. Many models may be used for the direct simulation, for examplea full field advection diffusion transport model using an LES closure,or reduced order models such as the gaussian plume model. From theresults of the direct simulation(s), the concentration of the targetanalytes at various time stamps of the simulated periods can beevaluated at the positions of the deployed sensor systems within thedigital twin simulation. It is then possible to form the relationalmatrix that relates emission variables to concentrations of analytes.The inverse relation can then be obtained with an inversion method aspresented in FIG. 12 and FIGS. 13A-B.

In particular, in order to identify a source of a target chemical at asite, a computer-implemented method may be performed. Referring now toFIG. 36 , a plan 3600 of an example site under monitoring isillustrated. The site may include equipment 3502, the plurality of airquality monitors 3504 (as mentioned before, air quality monitors mayalso be referred to as sensors, sensors systems, detectors in thisdisclosure). According to the method, the predominate air qualitymonitor 3504(1) may be provided which may include a first sensorresponsive to the target chemical and a first location information atwhich the predominate air quality monitor 3504(1) is located at thesite. Further, a first concentration of the target chemical at thepredominate air quality monitor 3504(1) may be measured as a function ofa wind speed and a wind direction. It may be noted that other factors(for, example air temperature, air pressure, etc.) other than the windspeed and the wind direction may be taken into consideration as well.The wind speed and the wind direction may be measured using a windsensor (e.g., an anemometer) which may be provided at the air qualitymonitor (as shown in FIG. 7C). The wind sensor may be located at thefirst location at the predominate air quality monitor 3504(1) or thesecond location at the secondary air quality monitor 3504(2). In case ofan emission, a plume 3508 of the emission (i.e., the target chemical)may occur at the site. Further, the wind speed and the wind directionmay be obtained from a wind rose diagram 3606.

In some configurations, the predominate air quality monitor 3504(1) mayinclude a second sensor responsive to a second chemical that isdifferent than the target chemical. Further, the method may includecreating a containment table defined as a composition of liquidcontained at each of the plurality of sources. The composition mayinclude at least the target chemical or the second chemical. A secondconcentration of the second chemical at the predominate air qualitymonitor 3504(1) may be measured, and the measurements of each of thetarget chemical and the second chemical to the containment table may becompared. The source of the target chemical or the second chemical maybe determined according to the containment table, and the identifiedsource may be outputted to a computer device (for example, the mobiledevice 152).

As mentioned earlier, the air quality monitors (i.e., the predominateair quality monitor 3504(1), the secondary air quality monitor 3504(2),etc.) may obtain measurements of the concentration of air samples atregular intervals (i.e., a predetermined frequency/cadence). Further,under some conditions, the frequency/cadence of obtaining themeasurements may be automatically increased or decreased for moreaccurate emission detection. To this end, in some configurations, awind-speed-threshold algorithm indicative of improved confidence ofsensor readings by the predominate air quality monitor 3504(1) and thesecondary air quality monitor 3504(2) may be predetermined. As such, thewind speed may be monitored, and the wind speed may be compared to thewind-speed-threshold. At the wind-speed-threshold, cadence of themeasuring of the first concentration may be increased.

In some configurations, a population of the actual emissionsmeasurements may be transmitted to the cloud server (e.g., “AWS”). Asmentioned above, some measurements may have noise, and therefore, maynot be suitable for performing the plume analysis and may be discarded.To this end, a highest first concentration of the population ofemissions measurements may be identified. Further, a lowest firstconcentration of the population of emissions measurements may beidentified. Furthermore, an SNR threshold may be determined. Forexample, an SNR ratio may be determined by dividing the firstconcentration by a difference between the highest first concentrationand the lowest first concentration. The individual readings of the firstconcentration that have an SNR ratio below the SNR threshold may bediscarded.

FIG. 37 illustrates a graphical representation 3700 of example SNRsassociated with different sensors (air quality monitors 3504). Forexample, an SNR for the predominate air quality monitor 3504(1) may bedepicted by a curve 3702, an SNR for the secondary air quality monitor3504(2) may be depicted by a curve 3704, an SNR for the tertiary airquality monitor 3504(3) may be depicted by a curve 3706, and an SNR foranother air quality monitor 3504 may be depicted by a curve 3708.Further, a threshold SNR of 0.7 may be selected. Based on this SNR, theair quality monitors with SNR<0.7 may be discarded. This is alreadyexplained in conjunction with FIGS. 27-29 .

According to the method, a horizontal distribution deviation defined asa standard deviation of a horizontal distribution of a plumeconcentration may be obtained. Similarly, a vertical distributiondeviation defined as a standard deviation of a vertical distribution ofthe plume concentration may be obtained. Further, according to themethod, at least one simulation model may be created for the site basedon simulation parameters. As mentioned above, the simulation parametersmay include at least two of a wind directions, a wind speed, an airpressure, an air temperature, a number of potential emission sources, alocation of each of the potential emission source, a source fluxassociated with each of the potential emission sources, a surfaceconcentration, a weather condition, a hygrometry data, an altitude, etc.

For example, as shown in the FIG. 36 , a first plume model 3602A and asecond plume model 3602B may be generated corresponding to two differentpotential emission sources, based on the various simulation parametersincluding wind direction and wind speed (as provided by the wind rosediagram 3606). Further, for example, the plume model 3602A may have aconfiguration 3604 (along y-plane).

An emission rate of the target chemical at the source may be identifiedusing the simulation model functionally which may be operated by thestandard deviation of horizontal distribution, the standard deviation ofvertical distribution, the first concentration at the predominate airquality monitor 3504(1), and the wind speed. The identified source maybe outputted to a computer device and displayed for a user.

Additionally, in some configurations, the secondary air quality monitor3504(2) may be provided that may include a second sensor responsive tothe target chemical, and a second location information at which thesecondary air quality monitor 3504(2) is located. A second concentrationof the target chemical at the secondary air quality monitor 3504(2) maybe measured as a function of the wind speed and the wind direction.According to the method, a first bearing of the source relative to thepredominate air quality monitor 3504(1) may be identified using thesimulation model. Further, a second bearing of the source relative tothe secondary air quality monitor 3504(2) may be identified using thesimulation model. Thereafter, in some configurations, coordinates of thelocation of source of the target chemical may be identified using thefirst bearing and the second bearing. Further, the source may beidentified from a plurality of possible sources of the target chemicalby correlating the identified coordinates of the source with theemission rate. The coordinates and the emission rate of the identifiedsource may be outputted to the computing device.

According to the method, a concentration profile may be built accordingto a plurality of inputs. The plurality of inputs may includeconcentration of emission and the wind direction. Further, a wind speeddependent variable may be created according to the concentration profilesourced as the wind speed fluctuates. The location of the emissionsource may be determined according to the plurality of concentrationprofiles effected by the wind speed.

Further, in some configurations, a maximum of the first concentration ofthe target chemical at the predominant air quality monitor along withthe wind direction may be logged. Further, a plume centerline may beestablished as the wind direction less 180 degrees from the location ofthe predominant air quality monitor, to thereby identify a direction ofthe source of the target chemical from the predominant air qualitymonitor.

In one embodiment, the composition of the emission may be an indicatorfor refining the localization of emission. Emissions may containdifferent compounds based on their origin in the site. Indeed, theproduct may be separated or transformed at the site. If multiple targetcompounds are monitored, the ratio of these may indicate differentprocesses.

For example, in natural gas extraction, natural gas from the well maycontain various compounds such as methane, heavier hydrocarbons such asethane, propane and butane, trace VOC such as H2S, Toluene, additivessuch as methanol (for preventing hydrate formation) and additional gasand liquids such as CO2 and water. This multiphase flow is thenseparated in the separator, and liquids are stored in tanks. As aresult, emission prior to separation, during separation, afterseparation and from the tanks may have different ratios and compositionof these compounds. If the sensor system can detect more than onecompound, or group of compounds, refinement can be obtained in processstep identification. For example, a VOC sensor may be used inconjunction with a methane sensor to differentiate emission within theprocess. Methane emissions with less VOC may come from post separationmethane gas while emission with more VOC may come from the tanks.

In one configuration, virtual emissions and/or simulation models createdby a mathematical model may be utilized. One illustrative mathematicalmodel is the Navier-Stokes function in fluid mechanics that describesthe flow of fluids (such as, for example, the flow of air). TheNavier-Stokes function may be derived from an equation devised by Swissmathematician Leonhard Euler to describe the flow of incompressible andfrictionless fluids. Other physicists and mathematicians (e.g., SirGeorge Gabriel Stokes) have evolved the model to improve both two-degreeand three-degree models. Complex vortices and turbulence, often referredto as chaos, that occur in three-dimensional fluid (including gas) flowsas velocities increase have proven intractable to any but approximatenumerical analysis methods. Examples of methods include Euler's originalequation, Guglielmo Marconi wireless communications models, Laplace'sequation, Gaussian Plume Model (GPM), Large Eddy Simulation (LES), andthe like. Navier-Stokes equations may be found and incorporated, such asthose—for example—found at https://en.wikipedia.org/wiki/Navier %E2%80%93Stokes_equations which is specifically incorporated by referencefor all that is disclosed and taught therein.

In one example, the present disclosure may include a sensor systemconfigured to monitor compounds in air and collocate weathermeasurements with self-powering, sample conditioning, edge processing,and/or communication capability.

In another example, the present disclosure may include a methodincluding: analyzing spectra for at least one of denoising, debiasing,peak alignment, speciation, or unknown compound and residual bias andnoise compensation.

In another example, the present disclosure may include a methodincluding detecting, localizing, and/or quantifying a site emissionusing a single static point sensor sensitive to at least one targetcompound and providing collocated measurement of weather.

Furthermore, the method may include the measurement of weather includesat least wind speed and wind direction determined based on atmosphericsimulations and inverse methods.

In another example, the present disclosure may include a methodincluding: qualifying of emission type using statistical inference,which at least distinguishes a normal emission from a leak.

In another example, the present disclosure may include a methodincluding detecting at least one emission; and determining whether atleast one emission is from one or more leaks.

In another example, the present disclosure may include a method for thecalculation of total site emissions.

In another example, the present disclosure may include a method for theestimation of total flux emission of landfills using one or more ofsurface concentrations and/or local weather measurements together with atransport simulation.

In another example, the present disclosure may include a method for theestimation of the detection area of a sensor system using transportsimulation.

In another example, the present disclosure may include a method foroptimizing a formation of sensor system networks relying on detectionthreshold and detection speed requirements together with a large scaletransport simulation.

In another example, the present disclosure may include a method totriage and report emission flags for maintenance based on theirlocation, quantification, and qualification.

In another example, the present disclosure may include an actionabilityengine for the tracking and suggestion of practices, equipment, andmanpower for proper leak maintenance.

In another example, the present disclosure may include a n actionabilityengine for the identification of repeat-offending components andcomponent types.

In another example, the present disclosure may include a n actionabilityengine for tracking of emission reduction goals.

In another example, the present disclosure may include a systemincluding a computing device including one or more processors and memorystoring instructions that, when executed by the one or more processors,cause the system to perform one of the methods described.

In another example, the present disclosure may include a system of claim15, further including one or more sensors in communication with thecomputing device and configured to detect one or more emissions, whereinthe computing device is configured to identify a leak based on thedetected one or more emissions.

The system may further include using one or more weather simulations andthe detected one or more emissions to identify the leak.

In another example, the present disclosure may include a methodincluding determining one or more characteristics of one or more siteemissions using a single static point sensor sensitive to at least onetarget compound and providing collocated measurement of weather. Inanother example, the present disclosure may further include quantifyingthe one or more site emissions by measuring a plume cross section acrossvarying wind. And, the method may further include quantifying the one ormore site emissions by estimating of an emission flux from concentrationand weather measurements using an inverse transport simulation of adigital twin.

In another example, the present disclosure may include a derivinglocalized site emissions concentration and weather measurements using aninverse transport simulation of a digital twin.

In another example, the present disclosure may include a methodincluding localizing site emissions by distinguishing between emissionsources at a zone, equipment group, and/or component level emissions.

In another example, the present disclosure may include a methodincluding: qualifying of emission type using statistical inference,which at least distinguishes a normal emission from a leak bycategorizing emission events based on their intensity, frequency and/orcomposition.

In another example, the present disclosure may include a method for theestimation of emission localization, quantification and localizationusing operational data streams, maintenance and inspection reports andraw inspection data.

In another example, the present disclosure may include a method for thecommoditization of emission estimation and measurement throughcertification, carbon credit and carbon offsets.

In another example, the present disclosure may include a method forpreventative maintenance scheduling based on emission estimation andmeasurements.

In another example, the present disclosure may include a method forcalculating and optimizing emission reduction costs for differentoperational strategies based on sensor measurements, geography,production data, equipment, maintenance data, labor costs, and/or otheravailable data. In another example, the present disclosure may furtherinclude a method including the sensor measurements include weather andchemical concentration measurements.

In another example, the present disclosure may include a method forgenerating an atmospheric digital twin using site metadata forsimulating atmospheric transport.

In another example, the present disclosure may include a method forquantifying error in localization and quantification of emissions of asite, using uncertainty quantification and prior probability of weathermeasurement precision and accuracy, modeling uncertainty and compoundsensing accuracy and precision.

Specific details are given in the above description to provide athorough understanding of the embodiments. However, it is understoodthat the embodiments may be practiced without these specific details.For example, circuits may be shown in block diagrams in order not toobscure the embodiments in unnecessary detail. In other instances,well-known circuits, processes, algorithms, structures, and techniquesmay be shown without unnecessary detail in order to avoid obscuring theembodiments.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

The controllers, computing devices, server devices, and other componentsof systems can include machine-readable media and one or moreprocessors, Programmable Logic Controllers, Distributed Control Systems,secure processors, memory, and the like. Secure storage may also beimplemented as a secure flash memory, secure serial EEPROM, secure fieldprogrammable gate array, or secure application-specific integratedcircuit. Processors can be standard central processing units or secureprocessors. Secure processors can be special-purpose processors that canwithstand sophisticated attacks that attempt to extract data orprogramming logic. A secure processor may not have debugging pins thatenable an external debugger to monitor the secure processor's executionor registers. In other embodiments, the system may employ a secure fieldprogrammable gate array, a smartcard, or other secure devices. Othertypes of computing devices can also be used.

Memory can include standard memory, secure memory, or a combination ofboth memory types. By employing a secure processor and/or secure memory,the system can ensure that both data and instructions are highly secure.Memory can be incorporated into the other components of the controllersystem and can store computer-executable or processor-executableinstructions, including routines executed by a programmable computingdevice. In some embodiments, the memory can store programs for presetconfigurations. Stored programs (e.g., simulation programs, calibrationprograms, graphic mapping programs, etc.) can be modified by a subject,operator, or remote manager to provide flexibility.

The present disclosure contemplates methods, systems, and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special-purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures, and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a machine, the machine properly views theconnection as a machine-readable medium. Thus, any such connection isproperly termed a machine-readable medium. Combinations of the above arealso included within the scope of machine-readable media.Machine-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing machines to perform a certain function orgroup of functions. The machine-readable media can be part of sensors,computing devices, or other components disclosed herein.

Unless the word “or” is expressly limited to mean only a single itemexclusive from the other items in reference to a list of two or moreitems, then the use of “or” in such a list is to be interpreted asincluding (a) any single item in the list, (b) all of the items in thelist, or (c) any combination of the items in the list. The term“comprising” is used throughout to mean including at least the recitedfeature(s) such that any greater number of the same feature and/oradditional types of other features are not precluded. It will also beappreciated that specific embodiments have been described herein forpurposes of illustration, but that various modifications may be madewithout deviating from the technology. Further, while advantagesassociated with certain embodiments of the technology have beendescribed in the context of those embodiments, other embodiments mayalso exhibit such advantages, and not all embodiments necessarily needto exhibit such advantages to fall within the scope of the technology.Accordingly, the disclosure and associated technology can encompassother embodiments not expressly shown or described herein. In general,in the following claims, the terms used should not be construed to limitthe claims to the specific embodiments disclosed in the specificationand the claims, but should be construed to include all possibleembodiments along with the full scope of equivalents to which suchclaims are entitled. Accordingly, the claims are not limited by thedisclosure.

Implementation of the techniques, blocks, steps, and means describedabove may be done in various ways. For example, these techniques,blocks, steps, and means may be implemented in hardware, software, or acombination thereof. For a digital hardware implementation, theprocessing units may be implemented within one or more applicationspecific integrated circuits (ASICs), digital signal processors (DSPs),digital signal processing devices (DSPDs), programmable logic devices(PLDs), field programmable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof. Foranalog circuits, they can be implemented with discreet components orusing monolithic microwave integrated circuit (MMIC), radio frequencyintegrated circuit (RFIC), and/or micro electro-mechanical systems(MEMS) technologies.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

The methods, systems, devices, graphs, and/or tables discussed hereinare examples. Various configurations may omit, substitute, or addvarious procedures or components as appropriate. For instance, inalternative configurations, the methods may be performed in an orderdifferent from that described, and/or various stages may be added,omitted, and/or combined. Also, features described with respect tocertain configurations may be combined in various other configurations.Different aspects and elements of the configurations may be combined ina similar manner. Also, technology evolves and, thus, many of theelements are examples and do not limit the scope of the disclosure orclaims. Additionally, the techniques discussed herein may providediffering results with different types of context awareness classifiers.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly or conventionally understood. As usedherein, the articles “a” and “an” refer to one or to more than one(i.e., to at least one) of the grammatical object of the article. By wayof example, “an element” means one element or more than one element.“About” and/or “approximately” as used herein when referring to ameasurable value such as an amount, a temporal duration, and the like,encompasses variations of ±20% or ±10%, ±5%, or +0.1% from the specifiedvalue, as such variations are appropriate to in the context of thesystems, devices, circuits, methods, and other implementations describedherein. “Substantially” as used herein when referring to a measurablevalue such as an amount, a temporal duration, a physical attribute (suchas frequency), and the like, also encompasses variations of ±20% or±10%, ±5%, or +0.1% from the specified value, as such variations areappropriate to in the context of the systems, devices, circuits,methods, and other implementations described herein.

As used herein, including in the claims, “and” as used in a list ofitems prefaced by “at least one of” or “one or more of” indicates thatany combination of the listed items may be used. For example, a list of“at least one of A, B, and C” includes any of the combinations A or B orC or AB or AC or BC and/or ABC (i.e., A and B and C). Furthermore, tothe extent more than one occurrence or use of the items A, B, or C ispossible, multiple uses of A, B, and/or C may form part of thecontemplated combinations. For example, a list of “at least one of A, B,and C” may also include AA, AAB, AAA, BB, etc.

While illustrative and presently preferred embodiments of the disclosedsystems, methods, and/or machine-readable media have been described indetail herein, it is to be understood that the inventive concepts may beotherwise variously embodied and employed, and that the appended claimsare intended to be construed to include such variations, except aslimited by the prior art. While the principles of the disclosure havebeen described above in connection with specific apparatuses andmethods, it is to be clearly understood that this description is madeonly by way of example and not as limitation on the scope of thedisclosure.

What is claimed is:
 1. An identification method for identifying a target chemical at a site, the identification method comprising: providing a sampling inlet to: capture a sample of air from an atmosphere surrounding the site; providing a compound sensor, comprising: a sample cell to receive the sample of air from the sampling inlet; a light source adjoined to the sample cell to emit a beam of light, wherein the beam of light passes through the sample cell; and a spectrophotometer adjoined to the sample cell; generating a first transmission spectrum of the beam of light with the spectrophotometer; transmitting a sample of air for interacting with the beam of light in the sample cell; generating a second transmission spectrum of the beam of light in response to interacting the sample of air with the beam of light with the spectrophotometer; processing the second transmission spectrum by: denoising and de-biasing the second transmission spectrum to generate a processed second transmission spectrum; generating an absorption spectrum of the beam of light by calculating absorbance of the beam of light by the sample of air based on: comparing the first transmission spectrum to the processed second transmission spectrum; and identifying the target chemical by decomposing the absorption spectrum to extract an absorption spectra related to the target chemical.
 2. The identification method of claim 1, wherein providing the sampling inlet further comprises: providing a pump to transfer the sample of air to the sample cell; and providing a filter for removing particulate matter and water present in the sample of air.
 3. The identification method of claim 1 and further comprising: storing the absorption spectra of the target chemical in a database.
 4. The identification method of claim 1 and further comprising: providing an entry point and an exit point for the sample of air on the sample cell; and naturally feeding the sample of air into the sample cell through the entry point and the exit point.
 5. The identification method of claim 1 and further comprising: adjoining an optical element to the sample cell, wherein the optical element is configured to collimate and focus the beam of light into the spectrophotometer.
 6. The identification method of claim 1 and further comprising: plotting a transmittance wavelength graph for the processed second transmission spectrum of the beam of light; identifying an absorption peak in the transmittance wavelength graph for the processed second transmission spectrum; and analyzing a shape, width, and position of the absorption peak to indicate a signature profile of the target chemical.
 7. The identification method of claim 6, wherein the shape, width, and position of the absorption peak is dependent on: an instrument transfer function of the spectrophotometer; and a temperature, a pressure and concentration of the target chemical in the sample of air.
 8. An identification system to identify a target chemical at a site, the identification system comprising: a sampling inlet to: capture a sample of air from an atmosphere surrounding the site; a compound sensor, comprising: a sample cell to receive the sample of air from the sampling inlet; a light source adjoined to the sample cell to emit a beam of light, wherein the beam of light passes through the sample cell and interacts with the sample of air; and a spectrophotometer adjoined to the sample cell to: generate a first transmission spectrum of the light; and generate a second transmission spectrum of the light in response to the beam of light interacting with the sample of air; and a logic control system connected to the compound sensor, configured to: process the second transmission spectrum by: denoising and debiasing the second transmission spectrum to generate a processed second transmission spectrum; generate an absorption spectrum by calculating absorbance, wherein the absorbance is calculated by comparing the first transmission spectrum to the processed second transmission spectrum; and identify the target chemical by decomposing the absorption spectrum to extract an absorption spectra related to the target chemical.
 9. The identification system of claim 8 wherein the sampling inlet further comprises: a pump to transfer the sample of air to the sample cell; and a filter for removing particulate matter and water present in the sample of air.
 10. The identification system of claim 8 and further comprising: a database configured to store the absorption spectra of the target chemical.
 11. The identification system of claim 8, wherein: the sample cell comprises an entry point and an exit point for the sample of air, wherein the sample of air is naturally fed in to the sample cell through the entry point and the exit point.
 12. The identification system of claim 8 and further comprising: an optical element adjoined to the sample cell, wherein the optical element is configured to collimate and focus the beam of light into the spectrophotometer.
 13. The identification system of claim 8, wherein the logic control system is configured to: plot a transmittance wavelength graph for the processed second transmission spectrum of the beam of light; identify an absorption peak in the transmittance wavelength graph for the processed second transmission spectrum; and analyze a shape, width, and position of the absorption peak to indicate a signature profile of the target chemical.
 14. The identification system of claim 13, wherein the shape, width, and position of the absorption peak is dependent on: an instrument transfer function of the spectrophotometer; and a temperature, a pressure and concentration of the target chemical in the sample of air. 